Automatically predicting shipper behavior using machine learning models

ABSTRACT

Embodiments are disclosed for autonomously predicting shipper behavior. An example method includes the following operations. One or more learning models are generated. Shipper behavior data for at least one shipper is extracted. The shipper behavior data includes a plurality of features associated with the at least one shipper scheduled to ship one or more parcels. It is predicted whether one or more shipments will be sent or arrive at a particular time based at least in part on running the plurality of features of the at least one shipper through the one or more learning models.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 62/589,821 entitled “SYSTEMS AND METHODS FOR AUTOMATICALLYPREDICTING SHIPPER BEHAVIOR USING MACHINE LEARNING MODELS,” filed Nov.22, 2017, which is incorporated herein by reference in its entirety.

FIELD

The present disclosure relates to using data analytics technology topredict shipper behavior, and, more particularly, to using gatheredshipper behavioral data and machine learning models to generate shipperbehavior predictions.

BACKGROUND

When parcels (e.g., packages, containers, letters, items, pallets or thelike) are received from shippers and transported from an origin to adestination, the process of transmitting the packages may include movingthe packages through various intermediate locations between its originand destination, such as sorting operation facilities. Processing andsorting at these facilities may include various actions, such as cullingwhere parcels are separated according to shape or other characteristics,capturing information from the parcel to retrieve shipping information(e.g., tracking number, destination address, etc.), organizing theparcels according to a shipment destination, and loading the parcelsinto a delivery vehicle. Resources such as human resources and equipmentare utilized to facilitate each step of the transportation of thepackage. Efficiently allocating resources throughout the chain ofdelivery depends on accurately predicting information about each leg ofthe transportation process.

Generating predictions regarding when a package will be sent or receivedfrom shippers has historically been unreliable. Analyzing packagemanifests (reports received from shippers before they send packages to acarrier) are one solution to this problem. However, conventional packagemanifest are subject to human error, and in reality, a shipper may notbehave exactly in accordance with the package manifest. Further,existing prediction technologies use static threshold scores to predictall shipping behavior, which makes the predictions less accurate andrelevant for any one given shipper. Accordingly, there is a need fortools that improved existing prediction technologies.

SUMMARY

Example embodiments described herein comprise systems that autonomouslypredicts shipper behavior. The details of some embodiments of thesubject matter described in this specification are set forth in theaccompanying drawings and the description below. Other features,aspects, and advantages of the subject matter will become apparent fromthe description, the drawings, and the claims. Various embodiments aredirected to an apparatus, a method, and a system for predicting shipperbehavior.

In some aspects, the apparatus for autonomously predicting shipperbehavior includes at least one non-transitory computer-readable storagemedium that includes program instructions. In some aspects, the programinstructions are executable by one or more processors to cause the oneor more processors to perform the following operations. One or moreshipper information units are accessed from a shipper behavioral datamanagement tool. The one or more shipper information units compriseshipper behavioral data associated with one or more shippers. Theshipper behavioral data comprises one or more of: package received time,manifest package time, and package information. One or more features areextracted from the one or more shipper information units. An outputcomprising a shipper behavior prediction for the shipper is generatedvia a shipper behavior learning model and the one or more features.

In some aspects, the method for autonomously predicting shipper behaviorincludes the following operations. One or more learning models aregenerated. Shipper behavior data for at least one shipper is extracted.The shipper behavior data includes a plurality of features associatedwith the at least one shipper scheduled to ship one or more parcels. Itis predicted whether one or more shipments will be sent or arrive at aparticular time based at least in part on running the plurality offeatures of the at least one shipper through the one or more learningmodels.

In some aspects, the system includes at least one computing devicehaving at least one processor and at least one computer readable storagemedium having program instructions embodied therewith. In some aspects,the program instructions are readable/executable by the at least oneprocessor to cause the system to perform the following operations. Oneor more learning models are generated. Shipper behavior data isextracted. The shipper behavior data includes a plurality of features ofat least one package associated with one or more shipping operations ofat least one shipper. A prediction output corresponding to a size of theat least one package is generated based at least in part on running theplurality of features of the at least one package through the one ormore learning models.

The above summary is provided merely for purposes of summarizing someexample embodiments to provide a basic understanding of some aspects ofthe invention. Accordingly, it will be appreciated that theabove-described embodiments are merely examples and should not beconstrued to narrow the scope or spirit of the invention in any way. Itwill be appreciated that the scope of the invention encompasses manypotential embodiments in addition to those here summarized, some ofwhich will be further described below.

BRIEF DESCRIPTION OF FIGS

Having thus described the disclosure in general terms, reference willnow be made to the accompanying drawings, which are not necessarilydrawn to scale, and wherein:

FIG. 1 provides an illustration of an exemplary embodiment of thepresent disclosure;

FIG. 2 provides a schematic of a shipper behavior predicting entityaccording to one embodiment of the present disclosure;

FIG. 3 provides an illustrative schematic representative of a mobilecomputing entity 110 that can be used in conjunction with embodiments ofthe present disclosure;

FIG. 4 illustrates an exemplary process for use with embodiments of thepresent disclosure;

FIG. 5 illustrates an exemplary process for use with embodiments of thepresent disclosure;

FIG. 6 illustrates an exemplary process for use with embodiments of thepresent disclosure;

FIG. 7 is an example block diagram of example components of an exampleshipper behavior learning model training environment;

FIG. 8 is an example block diagram of example components of an exampleshipper behavior learning model service environment;

FIG. 9 illustrates an example random forest learning model, in whichaspects of the present disclosure are employed in, according toparticular embodiments;

FIG. 10A illustrates a decision tree, in which aspects of the presentdisclosure are employed in, according to particular embodiments;

FIG. 10B illustrates another decision tree, in which aspects of thepresent disclosure are employed in, according to particular embodiments;

FIG. 10C illustrates yet another decision tree, in which aspects of thepresent disclosure are employed in, according to particular embodiments;

FIG. 11 is a schematic diagram of a directed acyclic graph, representinghow learning can occur, according to particular embodiments.

DETAILED DESCRIPTION

The present disclosure will now be described more fully hereinafter withreference to the accompanying drawings, in which some, but not allembodiments of the disclosure are shown. Indeed, the disclosure may beembodied in many different forms and should not be construed as limitedto the embodiments set forth herein. Rather, these embodiments areprovided so that this disclosure will satisfy applicable legalrequirements. Like numbers refer to like elements throughout.

I. OVERVIEW

Existing software technologies have various shortcomings. For example,various shipping prediction software technologies only compute data inresponse to manual user input. In an illustrative example, some softwaretechnologies display an identifier that prompts a first user to input aplanned package pick up time in his or her package manifest. In responseto the first user inputting these details into a field, existingtechnologies receive this data and process the data based on additionalmanual user input. For example, a second user (e.g., a carrierdriver/sorting facility worker) may determine if the planned pick uptime as indicated in the package manifest will be late, early, and/or ontime. Existing software technologies may include static identifiers orother graphical user interface (GUI) features that specify “late,”“early,” and “on time,” and prompt the second user to manually selectone of the identifiers or manually type whether the planned pickup timewill be late, early, or on time. The second user must manually inputthis manifest information each time these package manifests or otherdata is received from shippers. However, continuous manual input of thisinformation is time consuming, tedious, and often leads to inaccuratepredictions. For example, a user may have a history of inputting plannedpickup times that are much later than actual pickup times. Accordingly,in this situation existing technologies may only analyze the enteredplanned pickup time for a single delivery to make a prediction, insteadof analyzing historical user patterns, thereby making the predictioninaccurate.

Some existing software technologies also have shortcomings in that theycontinually prompt users to input the same information regardless ofwhat past user behavior, selections, or information have been receivedas input. Accordingly, these technologies may output the same displayedinformation or require users to input the same information every timethey receive a package manifest, for example. In an exampleillustration, the same user may always send a particular package on thesame day every month, which is received by a carrier entity around thesame time period. However, existing software technologies mayindefinitely, each time a package manifest or other shipper data isreceived, store a static indication or prediction (e.g., select a GUIbutton indicating package will be “late”) concerning when the packagewill be received. This can increase storage device I/O (e.g., excessphysical read/write head movements on non-volatile disk) because eachtime the system stores unnecessary and redundant information (e.g.,storing the same information that a package will be “late” after eachpackage manifest received from the same user every month), the computingsystem often has to repetitively reach out to the storage device toperform a read or write operation, which is time consuming, error prone,and can eventually wear on components, such as a read/write head.

Various embodiments of the present disclosure improve these existingsoftware technologies via new functionalities that these existingtechnologies or computing devices do not now employ. Further, variousembodiments improve various computer operations and resources (e.g.,disk I/O). For example, some embodiments improve existing softwaretechnologies by automating tasks (e.g., automatically predicting thesize of a parcel and/or when a parcel will be received) via certainrules. As described above, such tasks are not automated in variousexisting technologies and have only been historically performed bymanual input of human users. In particular embodiments, incorporatingthese certain rules (e.g., learning model branch node tests) improveexisting technological processes by allowing the automation of thesecertain tasks, which is described in more detail below. Particularembodiments also improve these existing software technologies bylearning (e.g., via machine learning models) past user behavior, such asselections and input, in order to generate and compute certain data,such as generating a prediction of a size of a parcel and/or when aparcel will be received. In this way, users do not have to keep manuallyentering the same information for each package manifest or other datareceived. Accordingly, because users do not have to keep manuallyentering the same information, storage device I/O is reduced. Forexample, a read/write head in various embodiments reduces the quantityof times it has to write data to a storage device, which may reduce thelikelihood of write errors and breakage of the read/write head. Someembodiments also improve these existing software technologies by some oreach of the processes, as described below with reference to FIGS. 1-11of the present disclosure.

II. COMPUTER PROGRAM PRODUCTS, METHODS, AND COMPUTING ENTITIES

Embodiments of the present disclosure may be implemented in variousways, including as computer program products that comprise articles ofmanufacture. A computer program product may include a non-transitorycomputer-readable storage medium storing applications, programs, programmodules, scripts, source code, program code, object code, byte code,compiled code, interpreted code, machine code, executable instructions,and/or the like (also referred to herein as executable instructions,instructions for execution, program code, and/or similar terms usedherein interchangeably). Such non-transitory computer-readable storagemedia include all computer-readable media (including volatile andnon-volatile media).

In one embodiment, a non-volatile computer-readable storage medium mayinclude a floppy disk, flexible disk, hard disk, solid-state storage(SSS) (e.g., a solid state drive (SSD), solid state card (SSC), solidstate module (SSM)), enterprise flash drive, magnetic tape, or any othernon-transitory magnetic medium, and/or the like. A non-volatilecomputer-readable storage medium may also include a punch card, papertape, optical mark sheet (or any other physical medium with patterns ofholes or other optically recognizable indicia), compact disc read onlymemory (CD-ROM), compact disc-rewritable (CD-RW), digital versatile disc(DVD), Blu-ray disc (BD), any other non-transitory optical medium,and/or the like. Such a non-volatile computer-readable storage mediummay also include read-only memory (ROM), programmable read-only memory(PROM), erasable programmable read-only memory (EPROM), electricallyerasable programmable read-only memory (EEPROM), flash memory (e.g.,Serial, NAND, NOR, and/or the like), multimedia memory cards (MMC),secure digital (SD) memory cards, SmartMedia cards, CompactFlash (CF)cards, Memory Sticks, and/or the like. Further, a non-volatilecomputer-readable storage medium may also include conductive-bridgingrandom access memory (CBRAM), phase-change random access memory (PRAM),ferroelectric random-access memory (FeRAM), non-volatile random-accessmemory (NVRAM), magnetoresistive random-access memory (MRAM), resistiverandom-access memory (RRAM), Silicon-Oxide-Nitride-Oxide-Silicon memory(SONOS), floating junction gate random access memory (FJG RAM),Millipede memory, racetrack memory, and/or the like.

In one embodiment, a volatile computer-readable storage medium mayinclude random access memory (RAM), dynamic random access memory (DRAM),static random access memory (SRAM), fast page mode dynamic random accessmemory (FPM DRAM), extended data-out dynamic random access memory (EDODRAM), synchronous dynamic random access memory (SDRAM), doubleinformation/data rate synchronous dynamic random access memory (DDRSDRAM), double information/data rate type two synchronous dynamic randomaccess memory (DDR2 SDRAM), double information/data rate type threesynchronous dynamic random access memory (DDR3 SDRAM), Rambus dynamicrandom access memory (RDRAM), Twin Transistor RAM (TTRAM), Thyristor RAM(T-RAM), Zero-capacitor (Z-RAM), Rambus in-line memory module (RIMM),dual in-line memory module (DIMM), single in-line memory module (SIMM),video random access memory (VRAM), cache memory (including variouslevels), flash memory, register memory, and/or the like. It will beappreciated that where embodiments are described to use acomputer-readable storage medium, other types of computer-readablestorage media may be substituted for or used in addition to thecomputer-readable storage media described above.

As should be appreciated, various embodiments of the present disclosuremay also be implemented as methods, apparatus, systems, computingdevices/entities, computing entities, and/or the like. As such,embodiments of the present disclosure may take the form of an apparatus,system, computing device, computing entity, and/or the like executinginstructions stored on a computer-readable storage medium to performcertain steps or operations. However, embodiments of the presentdisclosure may also take the form of an entirely hardware embodimentperforming certain steps or operations.

Embodiments of the present disclosure are described below with referenceto block diagrams and flowchart illustrations. Thus, it should beunderstood that each block of the block diagrams and flowchartillustrations may be implemented in the form of a computer programproduct, an entirely hardware embodiment, a combination of hardware andcomputer program products, and/or apparatus, systems, computingdevices/entities, computing entities, and/or the like carrying outinstructions, operations, steps, and similar words used interchangeably(e.g., the executable instructions, instructions for execution, programcode, and/or the like) on a computer-readable storage medium forexecution. For example, retrieval, loading, and execution of code may beperformed sequentially such that one instruction is retrieved, loaded,and executed at a time. In some exemplary embodiments, retrieval,loading, and/or execution may be performed in parallel such thatmultiple instructions are retrieved, loaded, and/or executed together.Thus, such embodiments can produce specifically-configured machinesperforming the steps or operations specified in the block diagrams andflowchart illustrations. Accordingly, the block diagrams and flowchartillustrations support various combinations of embodiments for performingthe specified instructions, operations, or steps.

III. EXAMPLE DEFINITIONS

As used herein, the terms “data,” “content,” “digital content,” “digitalcontent object,” “information,” and similar terms may be usedinterchangeably to refer to data capable of being transmitted, received,and/or stored in accordance with embodiments of the present disclosure.Thus, use of any such terms should not be taken to limit the spirit andscope of embodiments of the present disclosure. Further, where acomputing device is described herein to receive data from anothercomputing device, it will be appreciated that the data may be receiveddirectly from another computing device or may be received indirectly viaone or more intermediary computing devices/entities, such as, forexample, one or more servers, relays, routers, network access points,base stations, hosts, and/or the like, sometimes referred to herein as a“network.” Similarly, where a computing device is described herein totransmit data to another computing device, it will be appreciated thatthe data may be sent directly to another computing device or may be sentindirectly via one or more intermediary computing devices/entities, suchas, for example, one or more servers, relays, routers, network accesspoints, base stations, hosts, and/or the like.

The terms “package”, “parcel, “item,” and/or “shipment” refer to anytangible and/or physical object, such as a wrapped package, a container,a load, a crate, items banded together, an envelope, suitcases, vehicleparts, pallets, drums, vehicles, and the like sent through a deliveryservice from a first geographical location to one or more othergeographical locations.

The term “carrier” and/or “shipping service provider” (usedinterchangeably) refer to a traditional or nontraditionalcarrier/shipping service provider. A carrier/shipping service providermay be a traditional carrier/shipping service provider, such as UnitedParcel Service (UPS), FedEx, DHL, courier services, the United StatesPostal Service (USPS), Canadian Post, freight companies (e.g.truck-load, less-than-truckload, rail carriers, air carriers, oceancarriers, etc.), and/or the like. A carrier/shipping service providermay also be a nontraditional carrier/shipping service provider, such asAmazon, Google, Uber, ride-sharing services, crowd-sourcing services,retailers, and/or the like.

The term “shipper behavioral data” refers to data describing a shipper'sbehavior. In some embodiments, the shipper behavioral data comprises oneor more package received time, manifest package time, packageinformation such as tracking number, package activity time stamp,package dimension including height, length and width, package weight,package manifested weight, package manifest time stamp, package servicetype, package scanned time stamp, package tracking number, package sorttype code, package scanned code, unit load device type code, accountnumber associated with the package, and the like. In some embodiments,shipper behavioral data may be received from vehicles or mobilecomputing entities. In some embodiments, the shipper behavioral data isincluded in one or more package manifests.

The term “shipper behavior data management tool” refers to a managementtool that centrally collects and manages shipper behavior data. Theshipper behavior data may be provided by different service points,vehicles, mobile computing entities, and any other electronic devicesthat gather shipper behavior data. Alternatively or in addition, theshipper behavior data management tool may receive shipper behavior datadirectly from a distributed computing entity. In some embodiments, theshipper behavior data management tool is embedded within shipperbehavior predicting entity.

The term “shipper information units” refers to a set of data that hasbeen normalized and parsed based on shipper behavioral data. The processof parsing the shipper behavioral data may comprise selectively copyingshipper behavioral data based on the tuning of a shipper behaviorlearning model. For instance, in some embodiments, certain elements ofthe shipper behavioral data would not necessarily be needed by theshipper behavior learning model. The shipper information units in suchinstances refer to the subset of shipper behavioral data that does notcontain those certain elements of the shipper behavioral data, and theparsing and normalization process eliminates those certain elementsprior to the remaining data being fed into the shipper behavior learningmodel.

The term “feature” in various contexts refers to data generated based onshipper information units and subsequently fed into a machine learningmodel. In some embodiments, the features are equivalent to shipperinformation units. Alternatively or in addition, the features can begenerated by other techniques. For example, if the shipper informationunit comprises “manifest time: 9:00 am; received time: 10:04 am; packageweight: 30 lb”, the features generated can be based on categorization ofeach of the elements present in the shipper information unit in the formof “manifest time: morning; received time: morning; package weight:heavy”. In some embodiments, one feature may be generated based onmultiple shipper information units. For example, package received timefor multiple occasions can be used to generate one feature. A shipperbehavior prediction engine may use shipper information units thatrepresents package manifest time and package received time in the pasttwo months and generate a feature called “percentage of early manifestsin the past two months”.

The term “package manifest” refers to a report provided by a shipper toa shipping service provider that summarizes the shipment informationabout the package that the shipper is going to provide to the shippingservice provider. A package manifest may include the shipper's accountinformation, shipping record identifier, dimensions of the package to bepicked up, a planned package pick up time, a package pick up location,package weight, and the like.

The term “manifest package time” refers to the planned package pick upand/or delivery time in the package manifest. For example, a shipper mayrequest that a shipping service provider send a driver to pick up apackage at a certain location (manifest package location) at a manifestpackage time.

The term “package timeliness” refers to a shipper's timeliness inproviding the shipper's package to a shipping service provider withrespect to the manifest package time or other criteria. For example, ashipper may indicate that the shipper is going to provide a package tothe service provider on 2:00 pm on Thursday, and if the shipper providesthe shipping service provider with the package at 2:00 pm on Thursday,then the shipper would be categorized as a “timely shipper” with respectto that package. In some embodiments, providing the package within acertain window before or after 2:00 pm on Thursday would still result inthe shipper being categorized as a timely shipper. However, if theshipper provides the package late by a certain predefined amount oftime, the shipper would be categorized as a “late shipper” in someembodiments. And if the shipper provides the package early by a certainpredefined amount of time, then the shipper would be categorized as an“early shipper” in some embodiments.

The term “package received time” refers to the actual time where thepackage is received by a shipping service provider (e.g., a sortingfacility or a delivery driver) from a shipper or other entity.

The term “indicator” refers to data that indicates certain attributes.For example, a residential indicator indicates whether a package isbeing sent to residential address, a hazardous material indicatorindicates whether a package contains hazardous material, an oversizeindicator indicates whether a package is oversized, a document indicatorindicates whether a package is a document, and a Saturday deliveryindicator indicates whether a package is planned to be delivered on aSaturday.

The term “package activity time stamp” refers to a time stamp generatedbased on the time-stamp data acquired when performing packageactivities. For example, a package time activity time stamp may be atime stamp generated when the package is received from the shipper, atime stamp generated when the package is sent from a receiving site toan intermediate transmit vehicle, a time stamp generated when thepackage is sent from an intermediate transmit vehicle to anothervehicle, and the like.

The term “building type” refers to the categorization of a buildingoperated by a shipping service provider. For example, buildings may becategorized by size, average inbound and/or outbound volume, location,purpose of the building (intermediate transit or stores facingcustomers, etc.), and the like.

The term “service type” refers to the categorization of the serviceprovided associated with the package. For example, service type may becategorized by delivery speed, return receipt requested, insuranceassociated with the package, originating location, destination location,and the like. Exemplary service types include “Next Day Air”, “2^(nd)day Air”, “Worldwide Express”, “Standard”, and the like.

The term “sort type” refers to the categorization of time inhours/minutes of package received time. An exemplary way of definingsort type is provided as the following:

Package receive between 10:00 pm and 5:00 am: Sort type “Late night”;

Package receive between 5:00 am and 8:00 am: Sort type “Early Morning”;

Package receive between 8:00 am and 2:00 pm: Sort type “Morning to earlyafternoon”;

Package receive between 2:00 pm and 10:00 am: Sort type “Afternoon toNight”.

Packages can be categorized by sort types defined using different namesand different defined time period. Each defined category is called a“sort”.

The term “account type” refers to the categorization of the shipperaccount associated with the package. For example, account type may becategorized by whether the shipper is a personal shipper or a businessshipper, by the frequency with which the shipper provides packages, bythe service type requested, or by other shipping information associatedwith an account of the shipper. The shipping information may beprocessed before being used to categorize account type. For example, ifa personal shipper ships ten packages per month, a server may firstprocess the shipping information associated with the ten packages andgenerate an indicator of frequency of shipping for the shipper, thencategorize the shipper's account type as “frequent—personal shipper”.

The term “shipper behavior learning model,” “machine learning model,”“learning model” or the like refers to a machine learning model thatuses features generated from shipper information units and/or shipperbehavior data to predict shipper behavior. A machine learning model canencompass one or more input data (e.g., shipper information units),target variables, layers, classifiers, etc. In various embodiments, amachine learning model can receive an input (e.g., new package manifestdata/shipper behavior data), and based on the input identify patterns orassociations in order to predict a given output (e.g., classify ashipping package as small or large; classify a shipper as late or early,etc.). Machine learning models can be or include any suitable model,such as one or more: neural networks, word2Vec models, Bayesiannetworks, Random Forests, Boosted Trees, etc. “Machine learning” asdescribed herein, in particular embodiments, corresponds to algorithmsthat parse or extract features of historical data (e.g., historicalpackage manifests/shipper behavioral data/shipper information units),learn (e.g., via training) about the historical data by makingobservations or identifying patterns in the historical data, and thenreceive a subsequent input (e.g., a current package manifest/shipperbehavioral data/shipper information units) in order to make adetermination, prediction, and/or classification of the subsequent inputbased on the learning without relying on rules-based programming (e.g.,conditional statement rules).

The term “gradient boosting based learning model” refers to a machinelearning model for classification, regression and other tasks thatoperate by producing a prediction model in the form of an ensemble ofweak prediction models, typically decision trees.

The term “random forest” based learning model refers to a machinelearning model for classification, regression and other tasks thatoperate by constructing a multitude of decision trees at training timeand outputting the class or mean prediction of the individual trees.

The term “learning rate” refers to a weighting factor for thecorrections by new trees when added to a gradient boosting basedlearning model.

IV. EXAMPLE SYSTEM ARCHITECTURE

FIG. 1 provides an illustration of an exemplary embodiment of thepresent invention. As shown in FIG. 1 , this particular embodiment mayinclude one or more shipper behavior predicting entities 100 that eachcomprise a shipper behavior prediction engine, one or morepackage/items/shipments 102, one or more networks 105, one or morevehicles 107, one or more mobile computing entities 120, and/or thelike. Each of these components, entities, devices, systems, and similarwords used herein interchangeably may be in direct or indirectcommunication with, for example, one another over the same or differentwired or wireless networks. Additionally, while FIG. 1 illustrates thevarious system entities as separate, standalone entities, the variousembodiments are not limited to this particular architecture.

1. Exemplary Shipper Behavior Predicting Entity

FIG. 2 provides a schematic of a shipper behavior predicting entity 100according to one embodiment of the present invention. The shipperbehavior predicting entity 100 may comprise shipper behavioral datamanagement tool and shipper behavior predicting engine among othermodules. In certain embodiments, the shipper behavior predicting entity100 may be maintained by and/or accessible by a carrier. A carrier maybe a traditional carrier, such as United Parcel Service (UPS), FedEx,DHL, courier services, the United States Postal Service (USPS), CanadianPost, freight companies (e.g. truck-load, less-than-truckload, railcarriers, air carriers, ocean carriers, etc.), and/or the like. However,a carrier may also be a nontraditional carrier, such as Amazon, Google,Uber, ride-sharing services, crowd-sourcing services, retailers, and/orthe like. In general, the terms computing entity, computer, entity,device, system, and/or similar words used herein interchangeably mayrefer to, for example, one or more computers, computing entities,desktops, mobile phones, tablets, phablets, notebooks, laptops,distributed systems, gaming consoles (e.g., Xbox, Play Station, Wii),watches, glasses, iBeacons, proximity beacons, key fobs, radio frequencyidentification (RFID) tags, ear pieces, scanners, televisions, dongles,cameras, wristbands, kiosks, input terminals, servers or servernetworks, blades, gateways, switches, processing devices, processingentities, set-top boxes, relays, routers, network access points, basestations, the like, and/or any combination of devices or entitiesadapted to perform the functions, operations, and/or processes describedherein. Such functions, operations, and/or processes may include, forexample, transmitting, receiving, operating on, processing, displaying,storing, determining, creating/generating, monitoring, evaluating,comparing, and/or similar terms used herein interchangeably. In oneembodiment, these functions, operations, and/or processes can beperformed on data, content, information, and/or similar terms usedherein interchangeably.

As indicated, in one embodiment, the shipper behavior predicting entity100 may also include one or more communications interfaces 220 forcommunicating with various computing entities, such as by communicatingdata, content, information, and/or similar terms used hereininterchangeably that can be transmitted, received, operated on,processed, displayed, stored, and/or the like.

As shown in FIG. 2 , in one embodiment, the shipper behavior predictingentity 100 may include or be in communication with one or moreprocessing elements 305 (also referred to as processors, processingcircuitry, processing devices, and/or similar terms used hereininterchangeably) that communicate with other elements within the shipperbehavior predicting entity 100 via a bus, for example. As will beunderstood, the processing element 305 may be embodied in a number ofdifferent ways. For example, the processing element 305 may be embodiedas one or more complex programmable logic devices (CPLDs),microprocessors, multi-core processors, coprocessing entities,application-specific instruction-set processors (ASIPs),microcontrollers, and/or controllers. Further, the processing element305 may be embodied as one or more other processing devices orcircuitry. The term circuitry may refer to an entirely hardwareembodiment or a combination of hardware and computer program products.Thus, the processing element 305 may be embodied as integrated circuits,application specific integrated circuits (ASICs), field programmablegate arrays (FPGAs), programmable logic arrays (PLAs), hardwareaccelerators, other circuitry, and/or the like. As will therefore beunderstood, the processing element 305 may be configured for aparticular use or configured to execute instructions stored in volatileor non-volatile media or otherwise accessible to the processing element305. As such, whether configured by hardware or computer programproducts, or by a combination thereof, the processing element 305 may becapable of performing steps or operations according to embodiments ofthe present invention when configured accordingly. For example,processing element may be configured to perform various functionality ofa shipper behavior prediction engine, such as

In one embodiment, the shipper behavior predicting entity 100 mayfurther include or be in communication with non-volatile media (alsoreferred to as non-volatile storage, memory, memory storage, memorycircuitry and/or similar terms used herein interchangeably). In oneembodiment, the non-volatile storage or memory may include one or morenon-volatile storage or memory media 310, including but not limited tohard disks, ROM, PROM, EPROM, EEPROM, flash memory, MMCs, SD memorycards, Memory Sticks, CBRAM, PRAM, FeRAM, NVRAM, MRAM, RRAM, SONOS, FJGRAM, Millipede memory, racetrack memory, and/or the like. As will berecognized, the non-volatile storage or memory media may storedatabases, database instances, database management systems, data,applications, programs, program modules, scripts, source code, objectcode, byte code, compiled code, interpreted code, machine code,executable instructions, and/or the like. The terms database, databaseinstance, database management system, and/or similar terms used hereininterchangeably may refer to a structured collection of records or datathat is stored in a computer-readable storage medium, such as via arelational database, hierarchical database, hierarchical database model,network model, relational model, entity—relationship model, objectmodel, document model, semantic model, graph model, and/or the like.

In one embodiment, the shipper behavior predicting entity 100 mayfurther include or be in communication with volatile media (alsoreferred to as volatile storage, memory, memory storage, memorycircuitry and/or similar terms used herein interchangeably). In oneembodiment, the volatile storage or memory may also include one or morevolatile storage or memory media 215, including but not limited to RAM,DRAM, SRAM, FPM DRAM, EDO DRAM, SDRAM, DDR SDRAM, DDR2 SDRAM, DDR3SDRAM, RDRAM, TTRAM, T-RAM, Z-RAM, RIMM, DIMM, SIMM, VRAM, cache memory,register memory, and/or the like. As will be recognized, the volatilestorage or memory media may be used to store at least portions of thedatabases, database instances, database management systems, data,applications, programs, program modules, scripts, source code, objectcode, byte code, compiled code, interpreted code, machine code,executable instructions, and/or the like being executed by, for example,the processing element 305. Thus, the databases, database instances,database management systems, data, applications, programs, programmodules, scripts, source code, object code, byte code, compiled code,interpreted code, machine code, executable instructions, and/or the likemay be used to control certain aspects of the operation of the shipperbehavior predicting entity 100 with the assistance of the processingelement 305 and operating system.

As indicated, in one embodiment, the shipper behavior predicting entity100 may also include one or more communications interfaces 320 forcommunicating with various computing entities, such as by communicatingdata, content, information, and/or similar terms used hereininterchangeably that can be transmitted, received, operated on,processed, displayed, stored, and/or the like. Such communication may beexecuted using a wired data transmission protocol, such as fiberdistributed data interface (FDDI), digital subscriber line (DSL),Ethernet, asynchronous transfer mode (ATM), frame relay, data over cableservice interface specification (DOCSIS), or any other wiredtransmission protocol. Similarly, the shipper behavior predicting entity100 may be configured to communicate via wireless external communicationnetworks using any of a variety of protocols, such as general packetradio service (GPRS), Universal Mobile Telecommunications System (UMTS),Code Division Multiple Access 2000 (CDMA2000), CDMA2000 1× (1×RTT),Wideband Code Division Multiple Access (WCDMA), TimeDivision-Synchronous Code Division Multiple Access (TD-SCDMA), Long TermEvolution (LTE), Evolved Universal Terrestrial Radio Access Network(E-UTRAN), Evolution-Data Optimized (EVDO), High Speed Packet Access(HSPA), High-Speed Downlink Packet Access (HSDPA), IEEE 802.11 (Wi-Fi),Wi-Fi Direct, 802.16 (WiMAX), ultra wideband (UWB), infrared (IR)protocols, near field communication (NFC) protocols, Bluetoothprotocols, Wibree, Home Radio Frequency (HomeRF), Simple WirelessAbstract Protocol (SWAP), wireless universal serial bus (USB) protocols,and/or any other wireless protocol.

Although not shown, the shipper behavior predicting entity 100 mayinclude or be in communication with one or more input elements, such asa keyboard input, a mouse input, a touch screen/display input, motioninput, movement input, audio input, pointing device input, joystickinput, keypad input, and/or the like. The shipper behavior predictingentity 100 may also include or be in communication with one or moreoutput elements (not shown), such as audio output, video output,screen/display output, motion output, movement output, and/or the like.

In some embodiments, processing element 305, non-volatile memory 310 andvolatile memory 315 may be configured to support a shipper behaviorpredicting engine. For example, processing element 305 may be configuredto execute operations that comprise the shipper behavior predictingengine, and non-volatile memory 310 and volatile memory 315 may beconFIG. to store computer code executed by the processing element 305,as well as to store relevant intermediate or ultimate results producedfrom execution of the shipper behavior prediction engine.

In some embodiments, processing element 305, non-volatile memory 310 andvolatile memory 315 may be configured to support a shipper behavioraldata management tool. For example, processing element 305 may beconfigured to execute operations that comprise the shipper behavioraldata management tool, and non-volatile memory 310 and volatile memory315 may be conFIG. to store computer code executed by the processingelement 305, as well as to store relevant intermediate or ultimateresults produced from execution of the shipper behavioral datamanagement tool.

As will be appreciated, one or more of the shipper behavior predictingentity's 100 components may be located remotely from other shipperbehavior predicting entity 100 components, such as in a distributedsystem. Furthermore, one or more of the components may be combined andadditional components performing functions described herein may beincluded in the shipper behavior predicting entity 100. Thus, theshipper behavior predicting entity 100 can be adapted to accommodate avariety of needs and circumstances. As will be recognized, thesearchitectures and descriptions are provided for exemplary purposes onlyand are not limited to the various embodiments.

2. Exemplary Vehicle

In various embodiments, the term vehicle 107 is used generically. Forexample, a carrier/transporter vehicle 107 may be a manned or unmannedtractor, a truck, a car, a motorcycle, a moped, a Segway, a bicycle, agolf cart, a hand truck, a cart, a trailer, a tractor and trailercombination, a van, a flatbed truck, a vehicle, an unmanned aerialvehicle (UAV) (e.g., a drone), an airplane, a helicopter, a boat, abarge, and/or any other form of object for moving or transporting peopleand/or items/shipments (e.g., one or more packages, parcels, bags,containers, loads, crates, items banded together, vehicle parts,pallets, drums, the like, and/or similar words used hereininterchangeably). In one embodiment, each vehicle 107 may be associatedwith a unique vehicle identifier (such as a vehicle ID) that uniquelyidentifies the vehicle 107. The unique vehicle ID (e.g., trailer ID,tractor ID, vehicle ID, and/or the like) may include characters, such asnumbers, letters, symbols, and/or the like. For example, an alpha,numeric, or alphanumeric vehicle ID (e.g., “AS”) may be associated witheach vehicle 107. In another embodiment, the unique vehicle ID may bethe license plate, registration number, or other identifyinginformation/data assigned to the vehicle 107. As noted above, ininstances where the vehicle is a carrier vehicle, the vehicle may be aself-driving delivery vehicle or the like. Thus, for the purpose of thepresent disclosure, the term driver of a delivery vehicle may be used torefer to a carrier personnel who drives a delivery vehicle and/ordelivers items/shipments therefrom, an autonomous system configured todeliver items/shipments (e.g., a robot configured to transportitems/shipments from a vehicle to a service point such as a customer'sfront door or other service point), and/or the like.

Various computing entities, devices, and/or similar words used hereininterchangeably can be associated with the vehicle 107, such as a datacollection device or other computing entities. In general, the termscomputing entity, entity, device, system, and/or similar words usedherein interchangeably may refer to, for example, one or more computers,computing entities, desktops, mobile phones, tablets, phablets,notebooks, laptops, distributed systems, gaming consoles (e.g., Xbox,Play Station, Wii), watches, glasses, iBeacons, proximity beacons, keyfobs, RFID tags, ear pieces, scanners, televisions, dongles, cameras,wristbands, kiosks, input terminals, servers or server networks, blades,gateways, switches, processing devices, processing entities, set-topboxes, relays, routers, network access points, base stations, the like,and/or any combination of devices or entities adapted to perform thefunctions, operations, and/or processes described herein. The datacollection device may collect telematics information/data (includinglocation information/data) and transmit/send the information/data to anonboard computing entity, a distributed computing entity, and/or variousother computing entities via one of several communication methods.

In one embodiment, the data collection device may include, be associatedwith, or be in wired or wireless communication with one or moreprocessors (various exemplary processors are described in greater detailbelow), one or more location-determining devices or one or more locationsensors (e.g., Global Navigation Satellite System (GNSS) sensors), oneor more telematics sensors, one or more real-time clocks, a J-Busprotocol architecture, one or more electronic control modules (ECM), oneor more communication ports for receiving telematics information/datafrom various sensors (e.g., via a CAN-bus), one or more communicationports for transmitting/sending information/data, one or more RFIDtags/sensors, one or more power sources, one or more data radios forcommunication with a variety of communication networks, one or morememory modules 410, and one or more programmable logic controllers(PLC). It should be noted that many of these components may be locatedin the vehicle 107 but external to the data collection device.

In one embodiment, the one or more location sensors, modules, or similarwords used herein interchangeably may be one of several components inwired or wireless communication with or available to the data collectiondevice. Moreover, the one or more location sensors may be compatiblewith GPS satellites, such as Low Earth Orbit (LEO) satellite systems,Department of Defense (DOD) satellite systems, the European UnionGalileo positioning systems, Global Navigation Satellite systems(GLONASS), the Chinese Compass navigation systems, Indian RegionalNavigational satellite systems, and/or the like. Furthermore, the one ormore location sensors may be compatible with Assisted GPS (A-GPS) forquick time to first fix and jump start the ability of the locationsensors to acquire location almanac and ephemeris data, and/or becompatible with Satellite Based Augmentation System (SBAS) such as WideArea Augmentation System (WAAS), European Geostationary NavigationOverlay Service (EGNOS), and/or MTSAT Satellite Augmentation System(MSAS), GPS Aided GEO Augmented Navigation (GAGAN) to increase GPSaccuracy. This information/data can be collected using a variety ofcoordinate systems, such as the Decimal Degrees (DD); Degrees, Minutes,Seconds (DMS); Universal Transverse Mercator (UTM); Universal PolarStereographic (UPS) coordinate systems; and/or the like. Alternatively,triangulation may be used in connection with a device associated with aparticular vehicle 107 and/or the vehicle's operator and with variouscommunication points (e.g., cellular towers or Wi-Fi access points)positioned at various locations throughout a geographic area to monitorthe location of the vehicle 107 and/or its operator. The one or morelocation sensors may be used to receive latitude, longitude, altitude,heading or direction, geocode, course, position, time, and/or speed data(e.g., referred to herein as telematics information/data and furtherdescribed herein below). The one or more location sensors may alsocommunicate with the shipper behavior predicting entity, the datacollection device, distributed computing entity, user computing entity,and/or similar computing entities.

As indicated, in addition to the one or more location sensors, the datacollection device may include and/or be associated with one or moretelematics sensors, modules, and/or similar words used hereininterchangeably. For example, the telematics sensors may include vehiclesensors, such as engine, fuel, odometer, hubometer, tire pressure,location, weight, emissions, door, and speed sensors. The telematicsinformation/data may include, but is not limited to, speed data,emissions data, RPM data, tire pressure data, oil pressure data, seatbelt usage data, distance data, fuel data, idle data, and/or the like(e.g., referred to herein as telematics information/data). Thetelematics sensors may include environmental sensors, such as airquality sensors, temperature sensors, and/or the like. Thus, thetelematics information/data may also include carbon monoxide (CO),nitrogen oxides (NOx), sulfur oxides (SOx), Ethylene Oxide (EtO), ozone(O₃), hydrogen sulfide (H₂S) and/or ammonium (NH₄) data, and/ormeteorological data (e.g., referred to herein as telematicsinformation/data).

In one embodiment, the ECM may be one of several components incommunication with and/or available to the data collection device. TheECM, which may be a scalable and subservient device to the datacollection device, may have data processing capability to decode andstore analog and digital inputs from vehicle systems and sensors. TheECM may further have data processing capability to collect and presenttelematics information/data to the J-Bus (which may allow transmissionto the data collection device), and output standard vehicle diagnosticcodes when received from a vehicle's J-Bus-compatible on-boardcontrollers 440 and/or sensors.

As indicated, a communication port may be one of several componentsavailable in the data collection device (or be in or as a separatecomputing entity). Embodiments of the communication port may include anInfrared Data Association (IrDA) communication port, a data radio,and/or a serial port. The communication port may receive instructionsfor the data collection device. These instructions may be specific tothe vehicle 107 in which the data collection device is installed,specific to the geographic area in which the vehicle 107 will betraveling, specific to the function the vehicle 107 serves within afleet, and/or the like. In one embodiment, the data radio may beconfigured to communicate in accordance with multiple wirelesscommunication standards and protocols, such as UMTS, CDMA2000, 1×RTT,WCDMA, TD-SCDMA, LTE, E-UTRAN, EVDO, HSPA, HSDPA, Wi-Fi, WiMAX, UWB, IR,NFC, Bluetooth, USB, Wibree, HomeRF, SWAP, and/or the like.

3. Exemplary Package/Item/Shipment

A package/item/shipment 102 may be any tangible and/or physical object.Such items/shipments 102 may be picked up and/or delivered by acarrier/transporter. In one embodiment, an package/item/shipment 102 maybe or be enclosed in one or more packages, parcels, bags, containers,loads, crates, items banded together, vehicle parts, pallets, drums, thelike, and/or similar words used herein interchangeably. Suchitems/shipments 102 may include the ability to communicate (e.g., via achip (e.g., an integrated circuit chip), RFID, NFC, Bluetooth, Wi-Fi,and any other suitable communication techniques, standards, orprotocols) with one another and/or communicate with various computingentities for a variety of purposes. For example, thepackage/item/shipment 102 may be configured to communicate with a mobilecomputing entity 120 using a short/long range communication technology,as described in more detail below. Further, such package/items/shipments102 may have the capabilities and components of the described withregard to the occupancy computing entities 100, networks 105, vehicles107, user computing entities 120, and/or the like. For example, thepackage/item/shipment 102 may be configured to storepackage/item/shipment information/data. In example embodiments, thepackage/item/shipment information/data may comprise one or more of aconsignee name/identifier, an package/item/shipment identifier, aservice point (e.g., delivery location/address, pick-uplocation/address), instructions for delivering thepackage/item/shipment, an package/item/shipment delivery authorizationcode, information/data regarding if a mobile computing entity 120 ispresent at the service point, and/or the like. In this regard, in someexample embodiments, a package/item/shipment may communicate send “to”address information/data, received “from” address information/data,unique identifier codes, and/or various other information/data. In oneembodiment, each package/item/shipment may include apackage/item/shipment identifier, such as an alphanumeric identifier.Such package/item/shipment identifiers may be represented as text,barcodes, tags, character strings, Aztec Codes, MaxiCodes, DataMatrices, Quick Response (QR) Codes, electronic representations, and/orthe like. A unique package/item/shipment identifier (e.g., 123456789)may be used by the carrier to identify and track thepackage/item/shipment as it moves through the carrier's transportationnetwork. Further, such package/item/shipment identifiers can be affixedto items/shipments by, for example, using a sticker (e.g., label) withthe unique package/item/shipment identifier printed thereon (in humanand/or machine readable form) or an RFID tag with the uniquepackage/item/shipment identifier stored therein.

In various embodiments, the package/item/shipment information/datacomprises identifying information/data corresponding to thepackage/item/shipment. The identifying information/data may compriseinformation/data identifying the unique package/item/shipment identifierassociated with the package/item/shipment. Accordingly, upon providingthe identifying information/data to the package/item/shipment detaildatabase (may be embedded in distribution computing entity), thepackage/item/shipment detail database may query the storedpackage/item/shipment profiles to retrieve the package/item/shipmentprofile corresponding to the provided unique identifier.

Moreover, the package/item/shipment information/data may compriseshipping information/data for the package/item/shipment. For example,the shipping information/data may identify an origin location (e.g., anorigin serviceable point), a destination location (e.g., a destinationserviceable point), a service level (e.g., Next Day Air, Overnight,Express, Next Day Air Early AM, Next Day Air Saver, Jetline, Sprintline,Secureline, 2nd Day Air, Priority, 2nd Day Air Early AM, 3 Day Select,Ground, Standard, First Class, Media Mail, SurePost, Freight, and/or thelike), whether a delivery confirmation signature is required, and/or thelike. In certain embodiments, at least a portion of the shippinginformation/data may be utilized as identifying information/data toidentify a package/item/shipment. For example, a destination locationmay be utilized to query the package/item/shipment detail database toretrieve data about the package/item/shipment.

In certain embodiments, the package/item/shipment information/datacomprises characteristic information/data identifyingpackage/item/shipment characteristics. For example, the characteristicinformation/data may identify dimensions of the package/item/shipment(e.g., length, width, height), a weight of the package/item/shipment,contents of the package/item/shipment, and/or the like. In certainembodiments, the contents of the package/item/shipment may comprise aprecise listing of the contents of the package/item/shipment (e.g.,three widgets) and/or the contents may identify whether thepackage/item/shipment contains any hazardous materials (e.g., thecontents may indicate whether the package/item/shipment contains one ormore of the following: no hazardous materials, toxic materials,flammable materials, pressurized materials, controlled substances,firearms, and/or the like).

4. Exemplary User Computing Entity

Mobile computing entities 120 may be configured for autonomous operationand/or for operation by a user (e.g., a vehicle operator, deliverypersonnel, customer, and/or the like). In certain embodiments, mobilecomputing entities 120 may be embodied as handheld computing entities,such as mobile phones, tablets, personal digital assistants, and/or thelike, that may be operated at least in part based on user input receivedfrom a user via an input mechanism. Moreover, mobile computing entities120 may be embodied as onboard vehicle computing entities, such ascentral vehicle electronic control units (ECUs), onboard multimediasystem, and/or the like that may be operated at least in part based onuser input. Such onboard vehicle computing entities may be configuredfor autonomous and/or nearly autonomous operation however, as they maybe embodied as onboard control systems for autonomous or semi-autonomousvehicles, such as unmanned aerial vehicles (UAVs), robots, and/or thelike. As a specific example, mobile computing entities 120 may beutilized as onboard controllers for UAVs configured for picking-upand/or delivering packages to various locations, and accordingly suchmobile computing entities 120 may be configured to monitor variousinputs (e.g., from various sensors) and generated various outputs (e.g.,control instructions received by various vehicle drive mechanisms). Itshould be understood that various embodiments of the present disclosuremay comprise a plurality of mobile computing entities 120 embodied inone or more forms (e.g., handheld mobile computing entities 120,vehicle-mounted mobile computing entities 120, and/or autonomous mobilecomputing entities 120).

As will be recognized, a user may be an individual, a family, a company,an organization, an entity, a department within an organization, arepresentative of an organization and/or person, and/or the like—whetheror not associated with a carrier. In one embodiment, a user may operatea mobile computing entity 120 that may include one or more componentsthat are functionally similar to those of the shipper behaviorpredicting entities 100. FIG. 3 provides an illustrative schematicrepresentative of a mobile computing entity 120 that can be used inconjunction with embodiments of the present disclosure. In general, theterms device, system, computing entity, entity, and/or similar wordsused herein interchangeably may refer to, for example, one or morecomputers, computing entities, desktops, mobile phones, tablets,phablets, notebooks, laptops, distributed systems, vehicle multimediasystems, autonomous vehicle onboard control systems, watches, glasses,key fobs, radio frequency identification (RFID) tags, ear pieces,scanners, imaging devices/cameras (e.g., part of a multi-view imagecapture system), wristbands, kiosks, input terminals, servers or servernetworks, blades, gateways, switches, processing devices, processingentities, set-top boxes, relays, routers, network access points, basestations, the like, and/or any combination of devices or entitiesadapted to perform the functions, operations, and/or processes describedherein. Mobile computing entities 120 can be operated by variousparties, including carrier personnel (sorters, loaders, deliverydrivers, network administrators, and/or the like). As shown in FIG. 3 ,the mobile computing entity 120 can include an antenna 312, atransmitter 304 (e.g., radio), a receiver 306 (e.g., radio), and aprocessing element 308 (e.g., CPLDs, microprocessors, multi-coreprocessors, coprocessing entities, ASIPs, microcontrollers, and/orcontrollers) that provides signals to and receives signals from thetransmitter 304 and receiver 306, respectively.

The signals provided to and received from the transmitter 304 and thereceiver 306, respectively, may include signaling information inaccordance with air interface standards of applicable wireless systems.In this regard, the mobile computing entity 120 may be capable ofoperating with one or more air interface standards, communicationprotocols, modulation types, and access types. More particularly, themobile computing entity 120 may operate in accordance with any of anumber of wireless communication standards and protocols, such as thosedescribed above with regard to the shipper behavior predicting entities100. In a particular embodiment, the mobile computing entity 120 mayoperate in accordance with multiple wireless communication standards andprotocols, such as UMTS, CDMA2000, 1×RTT, WCDMA, TD-SCDMA, LTE, E-UTRAN,EVDO, HSPA, HSDPA, Wi-Fi, Wi-Fi Direct, WiMAX, UWB, IR, NFC, Bluetooth,USB, and/or the like. Similarly, the mobile computing entity 120 mayoperate in accordance with multiple wired communication standards andprotocols, such as those described above with regard to the shipperbehavior predicting entities 100 via a network interface 320.

Via these communication standards and protocols, the mobile computingentity 120 can communicate with various other entities using conceptssuch as Unstructured Supplementary Service information/data (USSD),Short Message Service (SMS), Multimedia Messaging Service (MMS),Dual-Tone Multi-Frequency Signaling (DTMF), and/or Subscriber IdentityModule Dialer (SIM dialer). The mobile computing entity 120 can alsodownload changes, add-ons, and updates, for instance, to its firmware,software (e.g., including executable instructions, applications, programmodules), and operating system.

According to one embodiment, the mobile computing entity 120 may includelocation determining aspects, devices, modules, functionalities, and/orsimilar words used herein interchangeably. For example, the mobilecomputing entity 120 may include outdoor positioning aspects, such as alocation module adapted to acquire, for example, latitude, longitude,altitude, geocode, course, direction, heading, speed, universal time(UTC), date, and/or various other information/data. In one embodiment,the location module can acquire information/data, sometimes known asephemeris information/data, by identifying the number of satellites inview and the relative positions of those satellites (e.g., using globalpositioning systems (GPS)). The satellites may be a variety of differentsatellites, including Low Earth Orbit (LEO) satellite systems,Department of Defense (DOD) satellite systems, the European UnionGalileo positioning systems, the Chinese Compass navigation systems,Indian Regional Navigational satellite systems, and/or the like. Thisinformation/data can be collected using a variety of coordinate systems,such as the Decimal Degrees (DD); Degrees, Minutes, Seconds (DMS);Universal Transverse Mercator (UTM); Universal Polar Stereographic (UPS)coordinate systems; and/or the like. Alternatively, the locationinformation can be determined by triangulating the mobile computingentity's 120 position in connection with a variety of other systems,including cellular towers, Wi-Fi access points, and/or the like.Similarly, the mobile computing entity 120 may include indoorpositioning aspects, such as a location module adapted to acquire, forexample, latitude, longitude, altitude, geocode, course, direction,heading, speed, time, date, and/or various other information/data. Someof the indoor systems may use various position or location technologiesincluding RFID tags, indoor beacons or transmitters, Wi-Fi accesspoints, cellular towers, nearby computing devices/entities (e.g.,smartphones, laptops) and/or the like. For instance, such technologiesmay include the iBeacons, Gimbal proximity beacons, Bluetooth Low Energy(BLE) transmitters, NFC transmitters, and/or the like. These indoorpositioning aspects can be used in a variety of settings to determinethe location of someone or something to within inches or centimeters.

The mobile computing entity 120 may also comprise a user interface (thatcan include a display 316 coupled to a processing element 308) and/or auser input interface (coupled to a processing element 308). For example,the user interface may be a user application, browser, user interface,and/or similar words used herein interchangeably executing on and/oraccessible via the mobile computing entity 120 to interact with and/orcause display of information from the shipper behavior predictingentities 100, as described herein. The user input interface can compriseany of a number of devices or interfaces allowing the mobile computingentity 120 to receive information/data, such as a keypad 318 (hard orsoft), a touch display, voice/speech or motion interfaces, or otherinput device. In some embodiments including a keypad 318, the keypad 318can include (or cause display of) the conventional numeric (0-9) andrelated keys (#, *), and other keys used for operating the mobilecomputing entity 120 and may include a full set of alphabetic keys orset of keys that may be activated to provide a full set of alphanumerickeys. In addition to providing input, the user input interface can beused, for example, to activate or deactivate certain functions, such asscreen savers and/or sleep modes.

As shown in FIG. 3 , the mobile computing entity 120 may also include ancamera, imaging device, and/or similar words used herein interchangeably326 (e.g., still-image camera, video camera, IoT enabled camera, IoTmodule with a low resolution camera, a wireless enabled MCU, and/or thelike) configured to capture images. The mobile computing entity 120 maybe configured to capture images via the onboard camera 326, and to storethose imaging devices/cameras locally, such as in the volatile memory322 and/or non-volatile memory 324. As discussed herein, the mobilecomputing entity 120 may be further configured to match the capturedimage data with relevant location and/or time information captured viathe location determining aspects to provide contextual information/data,such as a time-stamp, date-stamp, location-stamp, and/or the like to theimage data reflective of the time, date, and/or location at which theimage data was captured via the camera 326. The contextual data may bestored as a portion of the image (such that a visual representation ofthe image data includes the contextual data) and/or may be stored asmetadata associated with the image data that may be accessible tovarious computing entities.

The mobile computing entity 120 may include other input mechanisms, suchas scanners (e.g., barcode scanners), microphones, accelerometers, RFIDreaders, and/or the like configured to capture and store variousinformation types for the mobile computing entity 120. For example, ascanner may be used to capture package/item/shipment information/datafrom an item indicator disposed on a surface of a shipment or otheritem. In certain embodiments, the mobile computing entity 120 may beconfigured to associate any captured input information/data, forexample, via the onboard processing element 308. For example, scan datacaptured via a scanner may be associated with image data captured viathe camera 326 such that the scan data is provided as contextual dataassociated with the image data.

The mobile computing entity 120 can also include volatile storage ormemory 322 and/or non-volatile storage or memory 324, which can beembedded and/or may be removable. For example, the non-volatile memorymay be ROM, PROM, EPROM, EEPROM, flash memory, MMCs, SD memory cards,Memory Sticks, CBRAM, PRAM, FeRAM, NVRAM, MRAM, RRAM, SONOS, FJG RAM,Millipede memory, racetrack memory, and/or the like. The volatile memorymay be RAM, DRAM, SRAM, FPM DRAM, EDO DRAM, SDRAM, DDR SDRAM, DDR2SDRAM, DDR3 SDRAM, RDRAM, TTRAM, T-RAM, Z-RAM, RIMM, DIMM, SIMM, VRAM,cache memory, register memory, and/or the like. The volatile andnon-volatile storage or memory can store databases, database instances,database management systems, information/data, applications, programs,program modules, scripts, source code, object code, byte code, compiledcode, interpreted code, machine code, executable instructions, and/orthe like to implement the functions of the mobile computing entity 120.As indicated, this may include a user application that is resident onthe entity or accessible through a browser or other user interface forcommunicating with the shipper behavior predicting entities 100 and/orvarious other computing entities.

In another embodiment, the mobile computing entity 120 may include oneor more components or functionality that are the same or similar tothose of the shipper behavior predicting entities 100, as described ingreater detail above. As will be recognized, these architectures anddescriptions are provided for exemplary purposes only and are notlimiting to the various embodiments.

5. Exemplary Package/Item/Shipment Information

As noted herein, various shipments/items may have an associatedpackage/item/shipment profile, record, and/or similar words used hereininterchangeably stored in a package/item/shipment detail database. Thepackage/item/shipment profile may be utilized by the carrier to trackthe current location of the package/item/shipment and to store andretrieve information/data about the package/item/shipment. For example,the package/item/shipment profile may comprise electronic datacorresponding to the associated package/item/shipment, and may identifyvarious shipping instructions for the package/item/shipment, variouscharacteristics of the package/item/shipment, and/or the like. Theelectronic data may be in a format readable by various computingentities, such as a shipper behavior predicting entities 100, a mobilecomputing entity 110, an autonomous vehicle control system, and/or thelike. However, it should be understood that a computing entityconfigured for selectively retrieving electronic data within variouspackage/item/shipment profiles may comprise a format conversion aspectconfigured to reformat requested data to be readable by a requestingcomputing entity.

In various embodiments, the package/item/shipment profile comprisesidentifying information/data corresponding to the package/item/shipment.The identifying information/data may comprise information/dataidentifying the unique package/item/shipment identifier associated withthe package/item/shipment. Accordingly, upon providing the identifyinginformation/data to the package/item/shipment detail database, thepackage/item/shipment detail database may query the storedpackage/item/shipment profiles to retrieve the package/item/shipmentprofile corresponding to the provided unique identifier.

Moreover, the package/item/shipment profiles may comprise shippinginformation/data for the package/item/shipment. For example, theshipping information/data may identify an origin location (e.g., anorigin serviceable point), a destination location (e.g., a destinationserviceable point), a service level (e.g., Next Day Air, Overnight,Express, Next Day Air Early AM, Next Day Air Saver, Jetline, Sprintline,Secureline, 2nd Day Air, Priority, 2nd Day Air Early AM, 3 Day Select,Ground, Standard, First Class, Media Mail, SurePost, Freight, and/or thelike), whether a delivery confirmation signature is required, and/or thelike. In certain embodiments, at least a portion of the shippinginformation/data may be utilized as identifying information/data toidentify a package/item/shipment. For example, a destination locationmay be utilized to query the package/item/shipment detail database toretrieve data about the package/item/shipment.

In certain embodiments, the package/item/shipment profile comprisescharacteristic information/data identifying package/item/shipmentcharacteristics. For example, the characteristic information/data mayidentify dimensions of the package/item/shipment (e.g., length, width,height), a weight of the package/item/shipment, contents of thepackage/item/shipment, and/or the like. In certain embodiments, thecontents of the package/item/shipment may comprise a precise listing ofthe contents of the package/item/shipment (e.g., three widgets) and/orthe contents may identify whether the package/item/shipment contains anyhazardous materials (e.g., the contents may indicate whether thepackage/item/shipment contains one or more of the following: nohazardous materials, toxic materials, flammable materials, pressurizedmaterials, controlled substances, firearms, and/or the like).

VI. EXAMPLE SYSTEM OPERATION

FIG. 4-6 illustrates a flow diagram of an example system in accordancewith some embodiments discussed herein. It will be understood that eachblock of the flowcharts, and combinations of blocks in the flowcharts,may be implemented by various means, such as hardware, firmware,processor, circuitry, and/or other devices associated with execution ofsoftware including one or more computer program instructions. Forexample, one or more of the procedures described above may be embodiedby computer program instructions. In this regard, the computer programinstructions which embody the procedures described above may be storedby a memory of an apparatus employing an embodiment of the presentinvention and executed by a processor of the apparatus. As will beappreciated, any such computer program instructions may be loaded onto acomputer or other programmable apparatus (e.g., hardware) to produce amachine, such that the resulting computer or other programmableapparatus implements the functions specified in the flowchart blocks.These computer program instructions may also be stored in acomputer-readable memory that may direct a computer or otherprogrammable apparatus to function in a particular manner, such that theinstructions stored in the computer-readable memory produce an articleof manufacture, the execution of which implements the functionsspecified in the flowchart blocks. The computer program instructions mayalso be loaded onto a computer or other programmable apparatus to causea series of operations to be performed on the computer or otherprogrammable apparatus to produce a computer-implemented process suchthat the instructions executed on the computer or other programmableapparatus provide operations for implementing the functions specified inthe flowchart blocks.

FIG. 4 illustrates a flowchart that contain operations for using ashipper behavior predicting entity 100 to autonomously and automaticallypredict shipper behavior. The operations illustrated in FIG. 4 may, forexample, be performed by an apparatus 300 as described above. And inthis regard, the apparatus 100 may perform these operations through theuse of one or more of processing element 305, non-volatile memory 310,and volatile memory 315. It will be understood that the shipper behaviorpredicting engine comprises a set of hardware components or hardwarecomponents coupled with software components configured to autonomouslyor automatically predict shipper behavior. These components may, forinstance, utilize the processing element 305 to execute operations, andmay utilize non-volatile memory 310 to store computer code executed bythe processing element 305, as well as to store relevant intermediate orultimate results produced from execution of the shipper behaviorprediction engine. It should also be appreciated that, in someembodiments, the shipper behavior prediction engine may include aseparate processor, specially configured field programmable gate array(FPGA), or application specific interface circuit (ASIC) to perform itscorresponding functions. In addition, computer program instructionsand/or other type of code may be loaded onto a computer, processor orother programmable apparatus's circuitry to produce a machine, such thatthe computer, processor other programmable circuitry that execute thecode on the machine create the means for implementing the variousfunctions described in connection with the shipper behavior predictionengine.

At block 401, the shipper behavior prediction engine accesses one ormore shipper information units from a shipper behavioral data managementtool, wherein the one or more shipper information units comprise shipperbehavioral data associated with a shipper (e.g., historical actualshipping dates and package manifest dates of the same shipper), whereinthe shipper behavioral data comprises one or more of: package receivedtime, manifest package time, and package information. In someembodiments, the shipper behavioral data comprises package receivedtime, manifest package time, package information such as trackingnumber, package activity time stamp, package dimension including theheight, length and width of the package, package weight, packagemanifested weight, package manifest time stamp, package service type,package scanned time stamp, package tracking number, package sort typecode, package scanned code, unit load device type code, account numberassociated with the package, and the like. In some embodiments, shipperbehavioral data may be received from vehicles or mobile computingentities.

In some embodiments, accessing the shipper information units at block401 is associated with one or more rules in order for the process 400 toautomate. For example, a first rule may be, if one or more users (e.g.,the first 50 users) or other entity provides one or more packagemanifests (e.g., a batch of 100 package manifests), provides shippingtransaction data (e.g., data from one or more labels printed for a userat a shipping store), and/or any shipping data, the system automaticallyaccesses shipper information units at block 401, automatically extractsfeatures at block 402, and automatically generates output at block 403for the data provided by the user or other entity. In this way,particular embodiments improve existing technology by automatingfunctionality that was previously performed via manual computing systementry, such a user generating its own prediction value or entering avalue based on personal observation or manually inputting spreadsheetvalues for prediction. In an example illustration of particularembodiments, a rule may be that the process 400 will automatically occuronly after X time period (e.g., 20 days) for all data received (e.g.,100 package manifests). In this way automation can be batched or chunkedto reduce I/O cycles.

At block 402, the shipper behavior prediction engine extracts one ormore features (e.g., actual package receive times and associated packagemanifest projected receive times) from the one or more shipperinformation units, wherein the one or more features are representativeof one or more of a package received time, a manifest package time, orpackage information. In some embodiments, features are not necessarilyextracted according to block 402. Rather, block 403 can immediatelyfollow block 401 in particular embodiments. In some embodiments, theshipper behavior prediction engine also purposefully excludes otherfeatures from further analysis (e.g., address information, telephonenumber, package size, etc.) in parallel with the extracting in block402. In some embodiments, the features are generated by directly copyingshipper information units. Alternatively or in addition, the featurescan be generated from other techniques. For example, if a shipperinformation unit comprises “manifest time: 9:00 am; received time: 10:04am; package weight: 30 lb”, the features generated can be based onseparating each of the constituent elements present in the shipperinformation unit, and in this case, the first feature may be “manifesttime: morning”, the second feature may be “received time: morning”, andthe third feature may be “package weight: heavy”. In some embodiments,one feature may be generated based on multiple shipper informationunits. For example, package received time for multiple occasions can beused to generate one feature. A shipper behavior prediction engine mayuse shipper information units that represent package manifest time andpackage received time in the past two months and generate a featurecalled “percentage of early manifests in the past two months”.

At block 403, the shipper behavior prediction engine generates an outputcomprising a shipper behavior prediction for the shipper based onrunning the extracted features through a shipper behavior learningmodel. For example, the shipper behavior learning model can take theform of a random-forest based learning model, a gradient boosting basedlearning model, and the like in order to provide a probability that ashipper associated with the history of package manifests is a lateshipper, an early shipper, and/or a timely shipper, which may help asorting facility, for example, plan better for predicted workloads(e.g., increase staff in response to predicted received times).

In some embodiments, the learning models can be implemented usingprogramming languages such as R, Java, Python, Scala, C, Weka or C++,although other languages may be used in addition or in the alternative.Similarly, the learning models can be implemented using existingsoftware modules and framework such as Apache Spark, Apache Hadoop,Apache Storm, or Apache Flink, although other frameworks may be used inaddition or in the alternative. Additionally or alternatively, theshipper behavior learning model is capable of running on a cloudarchitecture, for example, on cloud architectures based on existingframeworks such as a Hadoop Distributed File System (HDFS) of a Hadoopcluster. In some embodiments, the cloud architectures are memory basedarchitectures where RAM can be used as long term storage to store datafor faster performance and better scalability compared to other types oflong term storage, such as a hard disk.

In some embodiments, a random-forest based learning model may be used,which operates by constructing a multitude of decision trees, evaluatingthe features by using the decision trees, and sampling a set of datafrom features as a training set. The decision trees starts with a rootnode representing a parameter, for example, “frequency of shipping >5 inthe past three months or not”. The decision tree can continue to growwith additional parameters. For example, under each decision tree, thedecision tree will grow or be traversed based on additional nodes withinthe decision tree with additional parameters such as “percentage of latemanifests in the past month >50% or not” and “percentage of latemanifests in the past months >50% or not” wherein the percentage of latemanifests are calculated by comparing the package manifest time andpackage received time. Each node with a parameter or test wouldrepresent a binary split or Boolean value where the features could belabeled as or associated with “Yes” (i.e., TRUE) or “No” (i.e. FALSE)for the particular node with the parameter or test. If input for a nodeis not selected from the results of any other node, then the node isdefined as the top layer. If a node is selected from the resultsproduced from an operation performed at a different node, the two nodesare in two different layers. In each layer, there might be a number ofdifferent nodes. In some embodiments, the parameters or tests used innodes are randomly selected from a set of pre-defined parameters. Thepre-defined parameters may be manually inputted into the shipperbehavior prediction engine. In some embodiments, the features fed intoone decision tree are a set of features that are associated with oneshipper. In some embodiments, if the frequency of shipping exceeds apre-defined threshold and the percentage of late manifests during apre-defined time period exceeds another pre-defined threshold, theshipper associated with this late manifest will be preliminarilycategorized as a “late shipper”. At each node of the decision tree,preliminary categorization may be generated. Note that this preliminarycategorization is a categorization used within in the learning model andmay not be equivalent to the final output of the model.

In some embodiments, the preliminary results from each node of decisiontree will be assigned a weight. The weight reflects how important thatparticular preliminary results is for the learning model. The decisiontree can grow deeper indefinitely by incorporating additional nodes withparameters that relates to the features extracted from shipperinformation units. For example, in one embodiment, the decision tree hasfour layers (in another words, the decision tree has a depth of four).The parameters used in the decision tree may be based on any featureextracted from the shipper information units. For example, a decisiontree can use parameters that comprises height, length and width, packageweight, package manifested weight, package manifest time, packageservice type, package scanned time stamp, package tracking number,package sort type code, package scanned code, unit load device typecode, account number associated with the package, and the like.

Various classification models can be used to generate parameters of thelearning models by automatically correlating features to one another,for example, logistic regression based models, naïve Bayes based models,support vector machines based models, quadratic classifiers, kernelestimation based models, boosting based models, decision tree basedmodels, and neural networks based models. In some embodiments, 16 to 25different types of features are selected as preliminary parameters. Thefeatures may be assigned or labeled by a weight value based onimportance. Accuracy of preliminary results can be generated for thedifferent preliminary parameters. Importance may be identified based onelbow method (comparing percentage of variance as a function of thenumber of parameters) or calculating Area Under the Receiving OperatingCharacteristic Curve (AUC). A number of parameters may be selected foruse by a random forest based model or other classification models. Insome embodiments, less than 16 or more than 25 types of features may beselected as preliminary parameters.

In some embodiments, the final result of a particular decision tree(note that this final result for the particular decision tree may or maynot be the final output of the learning model as a whole) could begenerated based on the preliminary result from each layer; the methodfor generating the final result may take the form of majority vote orweighted majority vote between the preliminary results. The output for aparticular iteration the random forest based learning model (note: notnecessarily the final output of random forest based learning model) maybe obtained by majority vote or weighted majority vote between theresults from different decision trees.

In some embodiments, the random forest based learning model may evaluatethe preliminary categorizations with the training set. The data in thetraining set may be categorized using the same categories as thepreliminary categorization by another learning model or may becategorized manually. Then the preliminary categorization is compared tothe preliminary categorization obtained by running the decision trees.The parameters of the learning model are thereafter adjusted based onthis comparison. For example, the “percentage of late manifests in thepast 1 month >50% or not” may be adjusted to “percentage of latemanifests in the past 1 month >40% or not”, and the like. Further, theweight of each node of decision trees may be adjusted. In someembodiments, the nodes and the parameters associated with the nodes canbe adjusted or deleted as well.

After completion of a defined number of rounds of analyzing featuresusing decision trees and/or adjusting the learning model with trainingset, the random forest based learning model may output a shipperbehavior prediction based on the outputs from the decision trees. Insome embodiments, the shipper behavior prediction is equivalent to thelatest version of output for a particular iteration of the random forestbased learning model.

In some embodiments, a gradient boosting based learning model mayalternatively be used. A gradient boosting based learning model alsoconstructs decision trees and utilizes decision trees in a similarfashion to a random forest based learning model. In some embodiments,the gradient boosting based learning models uses training set similar tohow the training sets are used in random forest based learning models.In some embodiments, the training sets comprises additional samplesselected based on the weights assigned to the parameters. In someembodiments, the gradient boosting based learning model comprises thefollowing parameters: a number of shipper classes, a maximum depth of atree, a learning rate, one or more probability estimates of the output,a logarithmic loss, and a number of rounds during which to apply a baselearning algorithm (one iteration of a multitude of decision trees).

In some embodiments, the shipper behavior prediction provided by themodel comprises one or more of: probability scores associated withclassifications of shippers such as early shipper, timely shipper andlate shipper for each shipper associated with set of features, averagetime difference between received time and manifest time, accountinformation associated with the shipper, time stamp data of indicatingwhen prediction is generated, building type associated with theprediction, predicted manifest and package received time at each sort,predicted of manifest and package received time at different definedtime frames such as day of the week, month of the year and the like,identifiers of buildings, probability score for building types,classifications or average package weight, indicators indicatingcorrelation between any of the shipper behavioral data in the form ofprobability scores, predicted averages and/or classifications, and thelike.

FIG. 5 illustrates a flowchart that contain operations for updating theshipper behavior prediction engine embedded in shipper behavior entity100. The operations illustrated in FIG. 5 may, for example, be performedby an apparatus 300 as described above. And in this regard, theapparatus 100 may perform these operations through the use of one ormore of processing element 305, non-volatile memory 310, and volatilememory 315. In various embodiments, FIG. 5 corresponds to updating thelearning model and prediction made at block 403 of FIG. 4 .

At block 501, the shipper behavior prediction engine receives additionalshipper behavioral data (e.g., additional package manifest data) after aparticular future time period. In some embodiments, the particularfuture time period reflects the time period when additional packages arereceived from shippers associated with the features used in operationsreflected in FIG. 4 . For example, if the features previously used togenerate predictions are associated with shippers A, B, C . . . ; theparticular future time period may be configured as time period whereadditional shipper behavioral data is received for the majority of theshipper A, B, C, etc. At block 502, the shipper behavior predictionengine extracts one or more features from the additional shipperbehavioral data. At block 503, the shipper behavior prediction engineupdates the shipper behavior prediction engine based on the featuresextracted from additional shipper behavioral data. For example, ashipper may have changed his/her shipping habits from historicallyshipping late (e.g., as indicated in FIG. 4 ) to now shipping early.Accordingly, the shipper behavior prediction engine may now predict thatthe shipper will ship early because of this recent trend. In someembodiments, the shipper behavior prediction engine updates itself bychanging the decision tree parameters or tests associated with theshipper behavior learning model. For example, instead of a test thatasks whether a user has shipped late in the last X months, the X can bechanged to a different value to reflect the user's sudden change withinthe last particular set of months.

FIG. 6 illustrates another flowchart that contain operations forupdating the shipper behavior prediction engine embedded in the shipperbehavior entity 100. The operations illustrated in FIG. 6 may, forexample, be performed by an apparatus 300 as described above. And inthis regard, the apparatus 100 may perform these operations through theuse of one or more of processing element 305, non-volatile memory 310,and volatile memory 315.

At block 601, the shipper behavior prediction engine receives additionalshipper behavioral data after a particular time period (e.g., after atime period of the prediction of block 403 and/or 503 of FIGS. 4 and 5respectively). At block 602, the shipper behavior prediction engineextracts one or more features from the additional shipper behavioraldata. At block 603, the shipper behavior prediction engine accesseshistorical data to generate a historical data set for one or morehistorical shipper behavior prediction. At block 604, the shipperbehavior prediction engine extracts one or more features from thehistorical data set. As illustrated, block 601 to 602 can happen before,after or concurrently with block 603 to 604. At block 605, the shipperbehavior prediction engine compares the features extracted from theadditional shipper behavioral data with the features extracted from thehistorical data set. At block 606, the shipper behavior predictionengine modifies the shipper behavior learning model stored in theshipper behavior prediction engine based on the difference between theone or more features extracted from the additional shipper behavioraldata and the one or more features extracted from the historical dataset. In some embodiments, the shipper behavior prediction modifies theshipper behavior learning model by reading inputs from an operator or alearning model analyzing the difference between the one or more featuresextracted from the additional shipper behavioral data and the one ormore features extracted from the historical data set.

FIG. 7 is an example block diagram of example components of an exampleshipper behavior learning model training environment 700 that is used totrain the shipper behavior learning model that is relied upon by theshipper behavior predicting entity 100 to update shipper learning modelin some example embodiments. The depicted shipper behavior learningmodel training environment 700 comprises a training engine 702, shipperbehavior learning model 710, and a shipper behavioral data managementtool 715.

In some examples, the shipper behavioral data management tool 715comprises a variety of shipper behavioral data. In some examples, thehistorical data may be obtained and/or stored after shipper behaviorpredicting entity 100 receives package received time data. For example,the shipper behavioral data management tool 715 may comprise historicalpackage received time data 720, shipper profile 722, package manifest724, package information 726, and/or other data 728.

In some embodiments, the shipper behavioral data comprises actualpackage received time (e.g., when a package was actually received from ashipper), manifest package time (e.g., when the shipper indicated thathe/she would ship the package), package information such as trackingnumber, package activity time stamp, package dimension including theheight, length and width of the package, package weight, packagemanifested weight, package manifest time stamp, package service type,package scanned time stamp, package tracking number, package sort typecode, package scanned code, unit load device type code, account numberassociated with the package, and the like. In some embodiments, shipperbehavioral data may be received from vehicles or mobile computingentities.

In some examples, the training engine 702 comprises a normalizationmodule 706 and a feature extraction module 704. The normalization module706, in some examples, may be configured to normalize the historicaldata so as to enable different data sets to be compared. In someexamples, the feature extraction module 704 is configured to parse theshipper behavioral data into shipper information units relevant tomodeling of the data, and non-shipper information units that are notutilized by the shipper behavior learning model 710, and then tonormalize each distinct shipper information units using differentmetrics. For example, the shipper information units can be labeled orcategorized based on package received time, package manifest time,package dimension, package weight, frequency of shipping from theparticular shipper associated with the package, building type, accounttype, sort type, other package information from scanners, other packageinformation from package manifest, other package information from mobilecomputing entities, and the like. For the purpose of categorizing theshipper information units, said information used to label or categorizeshipper information units may be processed (such as labeled, categorizedand parsed) first.

Alternatively or additionally, the normalization module 706 may beusable with respect to processing shipper behavioral data in the shipperbehavioral data management tool 715, such as to normalize the shipperbehavioral data before the shipper behavioral data is labeled orotherwise characterized by feature extraction module 704. For example,repetitive shipper behavioral data corresponding to the same instancereceived from multiple sources may be deduplicated.

Finally, the shipper behavior learning model 710 may be trained toextract one or more features from the historical data using patternrecognition, based on unsupervised learning, supervised learning,semi-supervised learning, reinforcement learning, association ruleslearning, Bayesian learning, solving for probabilistic graphical models,k-means based clustering, exponential smoothing, random forest modelbased learning, or gradient boosting model based learning, among othercomputational intelligence algorithms that may use an interactiveprocess to extract features from shipper behavioral data. In someembodiments, the shipper behavior learning model is a random forestbased learning model that has parameters comprising one or more of amaximum depth or a defined number of trees. In some embodiments, theshipper behavior learning model is a gradient boosting based learningmodel that comprises at least the following parameters: a number ofshipper classes, a maximum depth of a tree, a learning rate, probabilityestimates of the output, logarithmic loss, and a number of rounds duringwhich to apply a base learning algorithm. The probability estimates ofthe output may comprise confidence intervals, error estimates, and thelike. The logarithmic loss measures the performance of theclassification model.

FIG. 8 is an example block diagram of example components of an exampleshipper behavior learning model service environment 800. In some exampleembodiments, the example shipper behavior learning model serviceenvironment 800 comprises shipper behavioral data 810, a shipperbehavior prediction engine 830, output 840, the shipper behavioral datamanagement tool 715 and/or the shipper behavior learning model 710. Theshipper behavioral data management tool 715, a shipper behaviorprediction engine 830, and output 840 may take the form of, for example,a code module, a component, circuitry and/or the like. The components ofthe shipper behavior learning model service environment 800 areconfigured to provide various logic (e.g. code, instructions, functions,routines and/or the like) and/or services related to the shipperbehavior learning model service environment.

In some examples, the shipper behavioral data 810 comprises historicalpackage received time data, shipper profile, package manifest, packageinformation, and/or other data. In some examples, the shipper behavioraldata management tool 715 may be configured to normalize the raw inputdata, such that the data can be analyzed by the shipper behaviorprediction engine 830. In some examples the shipper behavioral datamanagement tool 715 is configured to parse the input data interaction togenerate one or more shipper information units. Alternatively oradditionally, the shipper behavior prediction engine 830 may beconfigured to extract one or more features from the one or more shipperinformation units. In some embodiments, the features are generated bydirectly copying shipper information units. Alternatively or inaddition, the features can be generated using other techniques. Forexample, if the shipper information unit comprises “manifest time: 9:00am; received time: 10:04 am; package weight: 30 lb”, the featuresgenerated can be based on categorization of each of the elements presentin the shipper information units in the form of “manifest time: morning;received time: morning; package weight: heavy”. In some embodiments, onefeature may be generated based on multiple shipper information units.For example, package received time for multiple occasions can be used togenerate one feature. A shipper behavior prediction engine may useshipper information units that represents package manifest time andpackage received time in the past two months and generate a featurecalled “percentage of early manifests in the past two months”.

In some examples, shipper behavioral data management tool 715 andshipper behavior prediction engine 830 are configured to receive ashipper behavior learning model, wherein the shipper behavior learningmodel was derived using a historical shipper behavioral data set.Alternatively or additionally, the shipper behavior prediction engine830 may be configured to generate generating an output 840 based on theshipper behavior learning model and the one or more features. In someembodiments, the output 840 comprises a shipper behavior prediction forthe shipper. Alternatively or additionally, the output 840 comprisesshipper behavior prediction for the shipper which comprises at least oneprobability score for the shipper, each probability score comprising aprobability that the shipper comprises a member of a correspondingshipper class. In some embodiments, classification of a particularshipper as a member of the different shipper classes is based on anexpected time difference between a package received time for theparticular shipper and manifest package time for the particular shipper.In some embodiments, the output 840 may further comprise probability ofpackage timeliness at sort.

Without shipper behavior predicting capabilities, a carrier would not beable to efficiently allocate resources with regard to package delivery.Such resources may comprise human resources and transportation resourcessuch as vehicles. The creation of package manifests (reports receivedfrom shipper before they send packages to a carrier) comprises a nascentsolution to this problem. However, package manifests are subject tohuman error and, in reality, a shipper may not behave exactly inaccordance with a package manifest. For example, the shipper's actualshipping time may be vastly different than the time indicated in thepackage manifest. Accordingly, there is a latent need for tools thatimprove the accuracy of shipper behavior prediction.

By providing shipper behavior prediction using shipper behaviorpredicting entity 100 to a computing entity configured to determineresource allocations, resources can be better allocated when packagemanifests are received. For example, if received package manifestsindicate that there are a lot of packages with manifest time after fourhours in certain locations, without shipper behavior prediction entity100, computing entities responsible for resource allocation would beassigning additional resources based on package those manifest times.However, the package manifest times are subject to human error and theadditional resources allocated may not all be needed in this scenario ifthe shippers are generally late in sending their packages to thecarrier, causing a waste of resources. Similarly, if the shippers aregenerally early, then failure to adequately outfit a carrier facilitycan cause understaffing problems when the shippers send the packagesearly and the additional resources are not available yet. Byautonomously and automatically predicting the behavior of variousshippers using shipper behavior predicting entity 100, a computingentity configured to determine resource allocations can reduce issuescaused by human error and mitigate potential resource misallocation.

Further, by utilizing shipper behavior prediction entity 100, acomputing entity configured to provide cost estimation associated witheach package will be able to provide more accurate cost estimation. Inturn, the pricing associated with each package from each shipper can beadjusted based on the more accurate cost estimation. For instance, whena shipper is typically early or late, a penalty surcharge may be appliedto incentivize timelier package delivery by the shipper and to pay forpossible resource allocation problems caused by the early and/or latepackage deliveries. More accurate cost estimation enables more accurateinternal evaluation and resource allocation within service facilities.

VII. EXAMPLE SYSTEM LEARNING MODELS

FIG. 9 illustrates an example random forest learning model 900,according to particular embodiments. Although FIG. 9 (and FIGS. 10-11 )illustrates a specific random forest learning model, values withspecific decision tree pathways, parameters, and tests, it is understoodthat any suitable value, node, test, and/or decision pathway may exist.It is also understood that although there is represented a specificquantity of decision trees with a particular quantity of nodes, theremay be any suitable quantity of decision trees and corresponding nodesin the learning model. In various embodiments, FIGS. 9-11 represent theshipper behavior learning model 710 of FIGS. 7 and 8 and/or used to makethe predictions as described in FIGS. 4-6 .

A random forest learning model includes various decision trees that eachpresent random and unique decision pathway tests to arrive at the sameset of results. More particularly, each decision tree within a randomforest has at least one different root or branch nodes and tests but thesame leaf node answers. Each decision tree is analyzed to determinewhich leaf node was traversed, as only one leaf node is traversed inparticular embodiments. The leaf node with the highest quantity oftraversals within the forest determines the output prediction (i.e.,majority vote wins). Each root node or branch node includes a “test”corresponding to a question that determines whether a TRUE or FALSEpathway is traversed. For example, referring to the root node 901, thetest or question is whether the percentage of manifests in the past Xmonths (e.g., 6 months) has been greater than or equal than 80%. If yesor TRUE, then there is traversal to the node 903, if no or FALSE thereis a traversal to node 905 for further processing. Accordingly, thetraversal of each decision tree starts at the root node, down throughthe branch nodes, until one of the leaf nodes are reached. The specificleaf nodes that are reached depends on the given tests within the rootand branch nodes. In various embodiments, each of these tests represent“rules” as described above that improve existing technologies in orderto automatically predict shipping behavior.

The learning model 900 includes decision tees 906, 904, and 902. Eachdecision tree has the same leaf node answers or values of “earlyshipper,” “late shipper,” and “timely shipper.” For example, thedecision tree 904 includes the leaf nodes 903, 907, and 911, whichrepresent “late shipper,” “timely shipper,” and “early shipper.”Identical leaf nodes are also indicated in the other decision tees 906and 902. The learning model 900 is used to generate a prediction ofwhether a particular shipment will be received (e.g., by a sortingfacility) at a “late” time, at a “timely” time,” or at an “early” time.Accordingly, the classifications of “late shipper,” “timely shipper,”and “early shipper” are generated to reflect this prediction. In anexample illustration, if a shipper is classified as an “early shipper,”it can be predicted that a sorting facility will receive the shipper'sshipped item earlier that some defined criteria, such as earlier thanthe stated package manifest time.

An example illustration of how each decision tree works is indicated bydecision tree 904. A particular first shipper (or set of shippers) mayhave a history of shipping parcels and thus may have generated variouspackage manifests and the system may have recorded when the shipperactually shipped packages. Accordingly, the system may compute and storethe percentage of early/late manifests that the first shipper has been apart of for the past X months. In this manner, the actual shipment timecan be compared to the package manifest information to determine if theshipper has historically been early, timely, and/or late. For example, auser may have brought a parcel to a carrier store for shipment laterthan the stated package manifest time 80% of the time over the past 24months. The root node 901 is used for deciding whether a shipper hasgenerated late manifests greater than or equal to 80% of the time duringthe last X months. If the particular shipper has made shipments thatwere late greater than or equal to 80% of the time, then the “TRUE”pathway is traversed (e.g., a Boolean value is set to TRUE) and thesystem automatically classifies (e.g., indicates a high probabilitythat) the shipper as a late shipper (branch node 903), meaning that theshipper will likely make his/her next shipment later than some criteria,such as indicated in a package manifest. However, if the particularshipper has made shipments that were late 79% of the time or less, thenthe FALSE pathway is traversed, whereby another decision at node 905 ismade to determine if the particular shipper is a timely shipper or earlyshipper based on another test. If the percentage of early manifests inthe past X months was not greater or equal to 70% (e.g., the userprovided a shipment to a shipping facility earlier than the indicatedpackage manifest only 60% of the time), then the FALSE pathway istraversed and the shipper is classified as a “timely shipper,” accordingto leaf node 907. Alternatively, if the percentage of early manifests inthe past X months was greater or equal to 70%, then the TRUE pathway istraversed and the shipper is classified as an “early shipper.” Thedecision tree 904 illustrates that the “winning” leaf node is node 903,indicating that the user is a “late shipper,” based on historical datathat indicates his percentage of late manifests has historically beengreater or equal to 80%. Classifying the particular shipper as a “late”shipper may help predict that the next shipment from the same and/oradditional users will likely be received later than the stated packagemanifest. Identical principles apply to “early” and “timely” shipperclassifications.

In various embodiments, the decision trees 906 and/or 902 includedifferent branch and/or root nodes and tests compared to the decisiontree 904, but have the same leaf nodes. Accordingly, for example,decision tree 906 can additionally or alternatively include a branch orroot node that has a test labeled, “the current month is December.” Ifthe current month is December, then a TRUE pathway can be traversed inorder to classify that a shipment will likely be late (i.e., a lateshipper) regardless of the history of the past X months. This may bebecause the particular user, for example, may have been an early and/ortimely shipper in every month except for the month of December duringthe past 4 years. Accordingly, the shipper may be classified as a “late”shipper in decision tree 906, but an “early” shipper according todecision tree 904. In another example, the decision tree 902 mayadditionally or alternatively include a root and/or branch node testthat is labeled “the shipment item is a large box.” For example, a usermay historically ship envelopes on time according to package manifestinformation, but may always ship large boxes late. Accordingly, if theitem to be shipped is a large box, the decision tree 902 may classifythe shipper as a “late” shipper, whereas if the item to be shipped is anenvelope, the decision tree 902 may classify the shipper as an “early”shipper.

FIG. 9 also illustrates that the majority vote winner is the “earlyshipper” classification. Decision tree 906 indicates that the shipperhas been classified as an “early shipper” as indicated by the dottedlines around the leaf node 908. However, the decision tree 904 indicatesthat the shipper has been classified as a “late shipper” as indicatedvia the dotted lines around the leaf node 903. The decision tree 902indicates that the shipper is classified as an “early shipper” asindicated by the dotted lines around the leaf node 110. Accordingly, thesystem tallies up the scores—there are 2 “early shipper” classificationscompared to only 1 “late shipper” classification. Because the majorityof decision trees indicate that the shipper is an “early shipper,” (2compared to 1) the system predicts that overall the shipper is an “earlyshipper” or that when a shipment package manifest is received for theshipper, the shipper will likely ship earlier than the package manifestand/or the parcel will be received earlier than expected.

FIGS. 10A-10C describe different decision trees of a single randomforest that are used to predict whether a received parcel will be asmall parcel (i.e., smalls), a large parcel (i.e., bigs), or anirregular parcel. An irregular parcel is a parcel that does not conformto a symmetrical dimensional shape, but is rather asymmetrical. Asymmetrical shape may be a package cube or rectangular prism with evendimensions on each side or an envelope. An asymmetrical shipping itemmay be a free-standing item that is not within any package, such as asmall statue, guitar, etc. In various embodiments, each decision treewithin FIGS. 10A-10C uses information in a package manifest in order totraverse the trees according to the tests.

FIG. 10A illustrates the decision tree 1000, in which aspects of thepresent disclosure are employed in. The root node 1001 is associatedwith a test that is used to determine whether a unit load device type isa bag. A “unit load device” is the mechanism that is used to load orcarry the item to be shipped. Examples of unit load devices includetrailers, bags, containers, etc. If the unit load device type is a bagper the test of the root node 1001, then the TRUE pathway is traversedto arrive at the leaf node 1003 labeled “smalls.” Alternatively, if theunit load device type is not a bag, then the FALSE pathway is traversedto arrive at the branch node 1005 associated with another test labeledthat the unit load device type is a trailer. If the item to be shippedby a customer is a trailer, then the TRUE pathway is traversed to arriveat the “bigs” leaf node 1007. Alternatively, if the unit load devicetype is not a trailer, then the FALSE pathway is traversed to arrive atthe “irregulars” leaf node 1009.

FIG. 10B illustrates the decision tree 1000-1, in which aspects of thepresent disclosure are employed in. The root node 1002 is associatedwith a test that is used to determine whether a package type is anenvelope or box. If the package type is not an envelope or box per thetest of the root node 1002, then the FALSE pathway is traversed toarrive at the leaf node 1004 labeled “irregulars.” Alternatively, if thepackage type is an envelope or box, then the TRUE pathway is traversedto arrive at the branch node 1006 associated with another test labeledthat the weight of the item to be shipped is less than or equal to X(e.g., 2 pounds). If the item to be shipped by a customer is less thanor equal to X, then the TRUE pathway is traversed to arrive at the“smalls” leaf node 1008. Alternatively, if the weight is not less thanor equal to X, then the FALSE pathway is traversed to arrive at the“bigs” leaf node 1010.

FIG. 10C illustrates the decision tree 1000-2. The root node 1021 isassociated with a test that is used to determine whether the length,width and/or height is less than Y values. If the length, width, and/orheight of a shipment parcel is less than Y values (e.g., length <1 foot,width <1 foot, height less than 2 feet) per the test of the root node1002, then the TRUE pathway is traversed to arrive at the leaf node 1025labeled “smalls.” Alternatively, if the length, width, and/or height isnot less than Y values, then the FALSE pathway is traversed to arrive atthe branch node 1023 associated with another test labeled that theweight of the item to be shipped is less than or equal to X (e.g., 2pounds). It is recognized that the branch node 1023 is the same asbranch node 1006 of FIG. 10B. Accordingly, in some embodiments the samenodes or tests are used in different decision trees notwithstanding thatother nodes or tests are different for each decision tree. If the itemto be shipped by a customer is less than or equal to X, then the TRUEpathway is traversed to arrive at the “irregulars” leaf node 1029.Alternatively, if the weight is not less than or equal to X, then theFALSE pathway is traversed to arrive at the “bigs” leaf node 1027.

FIG. 11 is a schematic diagram of a directed acyclic graph, representinghow learning can occur according to some embodiments. In embodiments,the acyclic graph corresponds to algorithms that are employed by thelearning model 710 of FIGS. 7 and 8 and/or used to make the predictionsas described in FIGS. 4-6 . It is understood that although the learningis illustrated by the directed acyclic graph in these embodiments, othermodels can be used instead of or in addition to the directed acyclicgraph. For example, learning can occur via one or more of: neuralnetworks, undirected graphs, linear regression models, logisticregression models, support vector machines, etc. It is also understoodthat the directed acyclic graph of FIG. 11 can include more or lessnodes with additional or different descriptors.

In some embodiments, the directed acyclic graph of FIG. 11 represents aBayesian network graph. A Bayesian network graph maps the relationshipsbetween nodes (i.e., events) in terms of probability. These graphs showhow the occurrence of particular events influence the probability ofother events occurring. Each node is also conditionally independent ofits non-descendants. These graphs follow the underlying principle ofBayes' theorem, represented as:

$\begin{matrix}{{{P\left( A \middle| B \right)} = \frac{{P\left( B \middle| A \right)}{P(A)}}{P(B)}},} & {{Equation}\mspace{14mu} 1}\end{matrix}$where A and B are events and P(B)≠0. That is, the probability (P) of Agiven B=the probability of B given A multiplied by the probability of(A) all over the probability of B.

The directed acyclic graph includes various nodes, directed edges, andconditional probability tables. The node 1104 and its conditionalprobability table 1104-1 illustrate that there is an 85% chance giventhe current circumstances that a shipper works in industry segment (B)(e.g. the airlines industry). This probability can be obtained, forexample, by obtaining information from a package manifest of a shipperand the shipper indicates the name of the company he/she works for.

The node 1102 and its conditional probability table 1102-1 indicate thatthere is only a <1% probability that the current manifest month isDecember. This probability or any other probability described herein canbe obtained, for example, by scraping historical calendaring informationoff of a user device, package manifest information, calendars, etc. Thenode 1106 traveling out of state (J) and its conditional probabilitytable 1106-1 show the probability that a shipper will be travelling outof state (J) given the variables (B) and/or (H) being true (occurring)or false (not occurring). The conditional probability table 1106-1illustrates that if (B) and (H) are both true, there is a 92% chancethat the shipper will travel out of state (J). If (B) is true and (H) isfalse, there is an 88% chance of (J) occurring. If (B) is false, and (H)is true, there is only a 26% chance of (J) occurring. If (B) and (P) areboth false, there is only a 16% chance of (J) occurring.

The node 1108 early shipper (R) and its conditional probability table1108-1 illustrate the probability that the shipper will be an earlyshipper (R) given that the shipper has or has not traveled out of thestate (J). The conditional probability table 1108-1 illustrates that if(J) is true, the shipper has a 24% probability that he/she will drop offhis/her package early or the package will be received early (e.g.,compared to the package manifest projected drop off time). Further, if(J) is false, the user has a 42% probability that the package will bedropped off/received early.

The node 1110 late shipper (M) and its conditional probability table1110-1 illustrate the probability that the user will be a late shipper(M) given that the user has or has not travelled out of state (J). Theconditional probability table 1110-1 illustrates that the probability of(M) occurring given that (J) is true is 96%. And the probability of (M)occurring given that (J) is not true is only 63%.

Each of these calculations (and any of the predictions described herein)can be used to generate predictions that a shipper item will besent/received at an early or late time and or other predictions, such assize of a package. This may help carrier staff members, such as sortingfacilities, prepare for a predicted high or low workload. For example,if it is predicted that various shipping items will be received at anunusually high rate for a particular month, the carrier team can ensurethat more workers will be available during the month.

In some embodiments, the methods, apparatus, and computer programproducts described herein comprise or utilize a shipper behaviorprediction engine configured to: access one or more shipper informationunits from a shipper behavioral data management tool, wherein the one ormore shipper information units comprise shipper behavioral dataassociated with a shipper, wherein the shipper behavioral data comprisesone or more of: package received time, manifest package time, andpackage information; extract one or more features from the one or moreshipper information units, wherein the one or more features arerepresentative of one or more of a package received time, a manifestpackage time, or package information; and generate, using a shipperbehavior learning model and the one or more features, an outputcomprising a shipper behavior prediction for the shipper.

Optionally, in some embodiments of the present disclosure, the shipperbehavior prediction for the shipper comprises at least one probabilityscore for the shipper, each of the at least one probability scorecomprising a probability that the shipper comprises a member of acorresponding shipper class.

Optionally, in some embodiments of the present disclosure, the shipperclass classifies the shipper as a timely shipper, an early shipper, or alate shipper.

Optionally, in some embodiments of the present disclosure, theclassification of a particular shipper as a member of the differentshipper classes is based on an expected time difference between apackage received time for the particular shipper and a manifest packagetime for the particular shipper.

Optionally, in some embodiments of the present disclosure, the shipperbehavior learning model is a random forest based learning model.

Optionally, in some embodiments of the present disclosure, the randomforest based learning model has parameters comprising one or more of amaximum depth or a defined number of trees.

Optionally, in some embodiments of the present disclosure, the shipperbehavior learning model is a gradient boosting based learning model.

Optionally, in some embodiments of the present disclosure, the gradientboosting based learning model comprises at least the followingparameters: a number of shipper classes, a maximum depth of a tree, alearning rate, one or more probability estimates of the output, alogarithmic loss, and a number of rounds during which to apply a baselearning algorithm.

Optionally, in some embodiments of the present disclosure, the outputfurther comprises a probability of package timeliness at sort.

Optionally, in some embodiments of the present disclosure, the shipperbehavior prediction engine is further configured to: receivingadditional shipper behavioral data after a particular future timeperiod; extracting one or more features from the additional shipperbehavioral data; and updating the shipper behavior prediction enginebased on the features extracted from additional shipper behavioral data.

Optionally, in some embodiments of the present disclosure, the system ormethod, further comprises a training engine configured to: receiveadditional shipper behavioral data after a particular future timeperiod; extract one or more features from the additional shipperbehavioral data; access historical data to generate a historical dataset for one or more historical shipper behavior prediction; extract oneor more features from the historical data set; comparing the one or morefeatures extracted from the additional shipper behavioral data with theone or more features extracted from the historical data set; modify theshipper behavior learning model stored in the shipper behaviorprediction engine based on the comparison of the one or more featuresextracted from the additional shipper behavioral data and said one ormore features extracted from the historical data set.

Optionally, in some embodiments of the present disclosure, the shipperbehavioral data comprises one or more tracking number, package activitytime stamp, package manifest time, service type, package dimension,package height, package width, package length, or account numberassociated with a shipper.

Optionally, in some embodiments of the present disclosure, the featuresextracted from the one or more shipper information units comprise one ormore of a residential indicator, a hazardous material indicator, anoversize indicator, a document indicator, a Saturday delivery indicator,a return service indicator, an origin location codes, a set ofdestination location codes, a package activity time stamp, a set ofscanned package dimensions, and a set of manifest package dimensions.

VIII. CONCLUSION

Many modifications and other embodiments of the inventions set forthherein will come to mind to one skilled in the art to which theseinventions pertain having the benefit of the teachings presented in theforegoing description and the associated drawings. Therefore, it is tobe understood that the inventions are not to be limited to the specificembodiments disclosed and that modifications and other embodiments areintended to be included within the scope of the appended claims.Although specific terms are employed herein, they are used in a genericand descriptive sense only and not for purposes of limitation, unlessdescribed otherwise.

What is claimed is:
 1. An apparatus for autonomously predicting shipperbehavior, the apparatus comprising: at least one non-transitorycomputer-readable storage medium that includes program instructions, theprogram instructions being executable by one or more processors to causethe one or more processors to: parse, via the one or more processorsexecuting a parsing module, raw shipper behavior data into a first setof shipper information units that are to be used by a shipper behaviorlearning model and a second set of shipper information units that arenot to be used by the shipper behavior learning model; in response tothe parsing, normalize, via the one or more processors executing anormalization module, the first set of information units; based on theparsing and the normalizing, train the shipper behavior model torecognize patterns within the first set of information units associatedwith one or more shipper classes; access the first set of shipperinformation units from a shipper behavioral data management tool,wherein the first set of shipper information units comprise shipperbehavioral data associated with a shipper, wherein the shipperbehavioral data comprises one or more of: package received time,manifest package time, or package information; extract one or morefeatures from the one or more shipper information units; and generate,via a shipper behavior learning model and the one or more features, anoutput comprising at least one probability score for the shipper, eachof the at least one probability score comprising a probability that theshipper comprises a member of a corresponding shipper class.
 2. Theapparatus of claim 1, wherein the features extracted from the first setof shipper information units comprise one or more of a residentialindicator, a hazardous material indicator, an oversize indicator, adocument indicator, a Saturday delivery indicator, a return serviceindicator, an origin location codes, a set of destination locationcodes, a package activity time stamp, a set of scanned packagedimensions, or a set of manifest package dimensions.
 3. The apparatus ofclaim 1, wherein the shipper class classifies the shipper as a timelyshipper, an early shipper, or a late shipper.
 4. The apparatus of claim3, wherein classification of a particular shipper as a member of thedifferent shipper classes is based on an expected time differencebetween a package received time for the particular shipper and a packagemanifest time for the particular shipper.
 5. The apparatus of claim 1,wherein the shipper behavior learning model includes a random forestbased learning model.
 6. The apparatus of claim 5, wherein the randomforest based learning model has parameters comprising one or more of amaximum depth or a defined number of trees.
 7. The apparatus of claim 1,wherein the shipper behavior learning model includes a gradient boostingbased learning model.
 8. The apparatus of claim 7, wherein the gradientboosting based learning model comprises at least the followingparameters: a number of shipper classes, a maximum depth of a tree, alearning rate, one or more probability estimates of the output, alogarithmic loss, and a number of rounds during which to apply a baselearning algorithm.
 9. The apparatus of claim 1, wherein the outputfurther comprises a probability of package timeliness at sort.
 10. Theapparatus of claim 1, wherein the program instructions further cause theone or more processors to: receive additional shipper behavioral datasubsequent to the accessing of the first set of shipper informationunits; extract one or more features from the additional shipperbehavioral data; and update the shipper behavior learning model based onthe features extracted from additional shipper behavioral data.
 11. Theapparatus of claim 1, wherein the program instructions further cause theone or more processors to: receive additional shipper behavioral datasubsequent to the accessing of the first set of shipper informationunits; extract one or more features from the additional shipperbehavioral data; access historical data to generate a historical dataset for one or more historical shipper behavior prediction; extract oneor more features from the historical data set; compare the one or morefeatures extracted from the additional shipper behavioral data with theone or more features extracted from the historical data set; and modifythe shipper behavior learning model stored in the shipper behavior basedon the comparison of the one or more features extracted from theadditional shipper behavioral data with the one or more featuresextracted from the historical data set.
 12. The apparatus of claim 1,wherein the shipper behavioral data comprises one or more of: trackingnumber, package activity time stamp, package manifest time, servicetype, package dimension, package height, package width, package length,or account number associated with a shipper.
 13. A system comprising: atleast one computing device having at least one processor; and at leastone computer readable storage medium having program instructionsembodied therewith, the program instructions readable/executable by theat least one processor to cause the system to: generate one or morelearning models; extract shipper behavior data, the shipper behaviordata includes a plurality of features of at least one package associatedwith one or more shipping operations of at least one shipper; andgenerate a prediction output corresponding to a size of the at least onepackage based at least in part on running the plurality of features ofthe at least one package through the one or more learning models.
 14. Amethod for autonomously predicting shipper behavior, the methodcomprising: training one or more learning models to recognize how timelyvarious shippers are; extracting shipper behavior data for at least oneshipper, the shipper behavior data includes a plurality of featuresassociated with the at least one shipper scheduled to ship one or moreparcels; and based on the training and the extracting, generating atleast one probability score for the at least one shipper, each of the atleast one probability score comprising a probability that the shippercomprises a member of a corresponding shipper class associated withtimeliness.
 15. The method of claim 14, wherein the shipper behavioraldata includes one or more of a residential indicator, a hazardousmaterial indicator, an oversize indicator, a document indicator, aSaturday delivery indicator, a return service indicator, an originlocation codes, a set of destination location codes, a package activitytime stamp, a set of scanned package dimensions, and a set of manifestpackage dimensions.
 16. The method of claim 14, wherein the shipperclass classifies the shipper as a timely shipper, an early shipper, or alate shipper.
 17. The method of claim 14, wherein the shipper behaviorlearning model is a gradient boosting based learning model.
 18. Themethod of claim 14, wherein the one or more learning models includes aplurality of decision trees, wherein each of the plurality of decisiontrees include a plurality of leaf nodes that are labelled as a timelyshipper, an early shipper, and a late shipper.